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Pathologists have depended on their visual experience to assess tissue structures in smear images, which was time-consuming, error-prone, and inconsistent. Deep learning, particularly Convolutional Neural Networks (CNNs), offers the ability to automate this procedure by recognizing patterns in tissue images. However, training these models necessitates huge amounts of labeled data, which can be difficult to come by due to the skill required for annotation and the unavailability of data, particularly for rare diseases. This work introduces a new semi-supervised method for tissue structure semantic segmentation in histopathological images. The study presents a CNN based teacher model that generates pseudo-labels to train a student model, aiming to overcome the drawbacks of conventional supervised learning approaches. Self-supervised training is used to improve the teacher model's performance on smaller datasets. Consistency regularization is integrated to efficiently train the student model on labeled data. Further, the study uses Monte Carlo dropout to estimate the uncertainty of proposed model. The proposed model demonstrated promising results by achieving an mIoU score of 0.64 on a public dataset, highlighting its potential to improve segmentation accuracy in histopathological image analysis. [ABSTRACT FROM AUTHOR]

Lung and colon cancers are common types of cancer with significant fatality rates. Early identification considerably improves the odds of survival for those suffering from these diseases. Histopathological image analysis is crucial for detecting cancer by identifying morphological anomalies in tissue samples. Regulations such as the HIPAA and GDPR impose considerable restrictions on the sharing of sensitive patient data, mostly because of privacy concerns. Federated learning (FL) is a promising technique that allows the training of strong models while maintaining data privacy. The use of a federated learning strategy has been suggested in this study to address privacy concerns in cancer categorization. To classify histopathological images of lung and colon cancers, this methodology uses local models with an Inception-V3 backbone. The global model is then updated on the basis of the local weights. The images were obtained from the LC25000 dataset, which consists of five separate classes. Separate analyses were performed for lung cancer, colon cancer, and their combined classification. The implemented model successfully classified lung cancer images into three separate classes with a classification accuracy of 99.867%. The classification of colon cancer images was achieved with 100% accuracy. More significantly, for the lung and colon cancers combined, the accuracy reached an impressive 99.720%. Compared with other current approaches, the proposed framework showed an improved performance. A heatmap, visual saliency map, and GradCAM were generated to pinpoint the crucial areas in the histopathology pictures of the test set where the models focused in particular during cancer class predictions. This approach demonstrates the potential of federated learning to enhance collaborative efforts in automated disease diagnosis through medical image analysis while ensuring patient data privacy. [ABSTRACT FROM AUTHOR]

Histopathological image analysis (HIA) plays an essential role in detecting cancer cell development, but it is time-consuming, prone to inaccuracy, and dependent on pathologist competence. This paper proposes an automated HIA that uses deep learning to improve accuracy and efficiency in liver cancer cell growth. The model uses whole slide image (WSI) input, open computer vision (OpenCV) libraries for image preprocessing, ResNet50 for patch-level feature extraction, and multiple instances learning for imagelevel classification. The suggested approach accurately distinguishes liver histopathological pictures as cancerous or non-cancerous. Assisting in the early detection of liver cancer cell development with potential invasion or spread. [ABSTRACT FROM AUTHOR]

In histopathological image analysis, the feature extraction task for classification proves to be demanding. This difficulty arises from the assortment of histological features appropriate for individual problems and the intricate presence of diverse geometric structures. The method proposed in this study leverages dictionary learning and sparse coding techniques to create priors tailored to specific targets, which is essential for classification purposes. Our approach introduces Penalized Sequential Discriminative Dictionary Learning (PSDDL), designed to integrate histopathological image features by acquiring structured, class-specific dictionaries. Initially, PSDDL constructs a dictionary from the input data, incorporating label information for each class. Subsequently, the proposed algorithm introduces a penalty and regularization term to amplify the efficacy of the acquired dictionary. Furthermore, the proposed method also tackles the class imbalance in the dataset by leveraging dictionary learning. Mainly, under-sampling is performed on the dataset, and the number of samples of fewer classes is kept for all categories and passed to the dictionary learning algorithm. Extensive experimental results highlight a notable enhancement in classification performance. The proposed structured discriminative dictionary learning technique consistently produces improved accuracies compared to other contemporary methods for classifying the BreakHis breast cancer dataset. Integrating class-specific information into the process of dictionary learning paves the way for enriching the interpretive capabilities of machine learning models while delving deeper into our comprehension of the complex and intricate structures inherent in biological tissues.

Breast cancer, a common malignancy impacting women globally, involves the uncontrolled growth of breast cancer cells. Timely identification and accurate classification of breast cancer into non-cancerous (benign) and cancerous (malignant) categories are crucial for effective treatment planning and enhanced patient outcomes. Conventional diagnostic techniques depend on histopathological examination of breast tissue samples, a process that can be subjective and time-consuming. The problem statement revolves around developing a computational model to automatically classify images from histopathology into non-cancerous or cancerous categories, addressing the limitations of manual diagnosis. Existing methodologies leverage various machine learning and deep learning techniques, particularly Convolutional Neural Networks (CNNs) being prominently utilized due to their effectiveness in image recognition tasks. However, these methods often require substantial computational resources and can suffer from overfitting due to the complex architecture. The objective of this study is to introduce an External Attention Transformer (EAT) model that utilizes external attention mechanisms, providing an approach to breast cancer image classification. This model aims to achieve high accuracy while maintaining computational efficiency. The primary metrics to assess the model’s performance include precision, recall, F1-score, and overall accuracy. The EAT model demonstrated outstanding performance achieving an accuracy of 99% on the BreaKHis dataset, indicating its potential as a reliable tool for breast cancer classification.

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is a conductive polymer commonly used in various technological applications. However, its impact on aquatic ecosystems remains largely unexplored. In this study, we investigated the toxicity effects of PEDOT:PSS on zebrafish. We first determined the lethal concentration (LC50) of PEDOT:PSS in zebrafish and then exposed AB-type zebrafish embryos to different concentrations of PEDOT:PSS for 120 h. Our investigation elucidated the toxicity effects of zebrafish development, including morphological assessments, heart rate measurements, behavioral analysis, transcriptome profiling, and histopathological analysis. We discovered that PEDOT:PSS exhibited detrimental effects on the early developmental stages of zebrafish, exacerbating the oxidative stress level, suppressing zebrafish activity, impairing cardiac development, and causing intestinal cell damage. This study adds a new dimension to the developmental toxicity of PEDOT:PSS in zebrafish. Our findings contribute to our understanding of the ecological repercussions of PEDOT:PSS and highlight the importance of responsible development and application of novel materials in our rapidly evolving technological landscape. [ABSTRACT FROM AUTHOR]

Lung and colon cancers are common types of cancer with significant fatality rates. Early identification considerably improves the odds of survival for those suffering from these diseases. Histopathological image analysis is crucial for detecting cancer by identifying morphological anomalies in tissue samples. Regulations such as the HIPAA and GDPR impose considerable restrictions on the sharing of sensitive patient data, mostly because of privacy concerns. Federated learning (FL) is a promising technique that allows the training of strong models while maintaining data privacy. The use of a federated learning strategy has been suggested in this study to address privacy concerns in cancer categorization. To classify histopathological images of lung and colon cancers, this methodology uses local models with an Inception-V3 backbone. The global model is then updated on the basis of the local weights. The images were obtained from the LC25000 dataset, which consists of five separate classes. Separate analyses were performed for lung cancer, colon cancer, and their combined classification. The implemented model successfully classified lung cancer images into three separate classes with a classification accuracy of 99.867%. The classification of colon cancer images was achieved with 100% accuracy. More significantly, for the lung and colon cancers combined, the accuracy reached an impressive 99.720%. Compared with other current approaches, the proposed framework showed an improved performance. A heatmap, visual saliency map, and GradCAM were generated to pinpoint the crucial areas in the histopathology pictures of the test set where the models focused in particular during cancer class predictions. This approach demonstrates the potential of federated learning to enhance collaborative efforts in automated disease diagnosis through medical image analysis while ensuring patient data privacy.

This study contributes to the Health 4.0 paradigm by enhancing the precision of cell nuclei detection in histopathological images, a critical step in digital pathology. The presented approach is characterized by the combination of deep learning with traditional analytic classifiers. Traditional methods in histopathology rely heavily on manual inspection by expert histopathologists. While deep learning has revolutionized this process by offering rapid and accurate detections, its black-box nature often results in a lack of interpretability. This can be a significant hindrance in clinical settings where understanding the rationale behind predictions is crucial for decision-making and quality assurance. Our research addresses this gap by employing the YOLOv5 framework for initial nuclei detection, followed by an analysis phase where poorly performing cases are isolated and retrained to enhance model robustness. Furthermore, we introduce a logistic regression classifier that uses a combination of color and textural features to discriminate between satisfactorily and unsatisfactorily analyzed images. This dual approach not only improves detection accuracy but also provides insights into model performance variations, fostering a layer of interpretability absent in most deep learning applications. By integrating these advanced analytical techniques, our work aligns with the Health 4.0 initiative's goals of leveraging digital innovations to elevate healthcare quality. This study paves the way for more transparent, efficient, and reliable digital pathology practices, underscoring the potential of smart technologies in enhancing diagnostic processes within the Health 4.0 framework. [Display omitted] • This paper addresses analysis of histopathological images for cell nuclei detection. • An ensemble of two deep learning models is proposed to improve the performance. • Initial step involves training a deep learning model using all available images. • Results are assessed and categorized based on the model's performance. • Images with worse results are identified, augmented and used to train a new model. • A logistic regression classifier reproduces the data division. • The input features of this classifier offer valuable insights for pathologists. [ABSTRACT FROM AUTHOR]

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is a conductive polymer commonly used in various technological applications. However, its impact on aquatic ecosystems remains largely unexplored. In this study, we investigated the toxicity effects of PEDOT:PSS on zebrafish. We first determined the lethal concentration (LC50) of PEDOT:PSS in zebrafish and then exposed AB-type zebrafish embryos to different concentrations of PEDOT:PSS for 120 h. Our investigation elucidated the toxicity effects of zebrafish development, including morphological assessments, heart rate measurements, behavioral analysis, transcriptome profiling, and histopathological analysis. We discovered that PEDOT:PSS exhibited detrimental effects on the early developmental stages of zebrafish, exacerbating the oxidative stress level, suppressing zebrafish activity, impairing cardiac development, and causing intestinal cell damage. This study adds a new dimension to the developmental toxicity of PEDOT:PSS in zebrafish. Our findings contribute to our understanding of the ecological repercussions of PEDOT:PSS and highlight the importance of responsible development and application of novel materials in our rapidly evolving technological landscape.

Invasive carcinoma of no special type (IC-NST) is known to be one of the most prevalent kinds of breast cancer, hence the growing research interest in studying automated systems that can detect the presence of breast tumors and appropriately classify them into subtypes. Machine learning (ML) and, more specifically, deep learning (DL) techniques have been used to approach this problem. However, such techniques usually require massive amounts of data to obtain competitive results. This requirement makes their application in specific areas such as health problematic as privacy concerns regarding the release of patients' data publicly result in a limited number of publicly available datasets for the research community. This paper proposes an approach that leverages federated learning (FL) to securely train mathematical models over multiple clients with local IC-NST images partitioned from the breast histopathology image (BHI) dataset to obtain a global model. First, we used residual neural networks for automatic feature extraction. Then, we proposed a second network consisting of Gabor kernels to extract another set of features from the IC-NST dataset. After that, we performed a late fusion of the two sets of features and passed the output through a custom classifier. Experiments were conducted for the federated learning (FL) and centralized learning (CL) scenarios, and the results were compared. Competitive results were obtained, indicating the positive prospects of adopting FL for IC-NST detection. Additionally, fusing the Gabor features with the residual neural network features resulted in the best performance in terms of accuracy, F1 score, and area under the receiver operation curve (AUC-ROC). The models show good generalization by performing well on another domain dataset, the breast cancer histopathological (BreakHis) image dataset. Our method also outperformed other methods from the literature. [ABSTRACT FROM AUTHOR]

Background: Early detection of lymph node metastasis in breast cancer is vital for improving treatment outcomes and prognosis., Methods: This study introduces an Improved Decompose, Transfer, and Compose Binary Coyote Net-based Multiple Instance Learning (ImDeTraC-BCNet-MIL) method for predicting lymph node metastasis from Whole Slide Images (WSIs) using multiple instance learning. The method involves segmenting WSIs into patches using Otsu and double-dimensional clustering techniques. The developed multiple instance learning approach introduces a paradigm into computational pathology by shaping pathological data and constructing features. ImDeTraC-BCNet-MIL was utilised for feature generation during both training and testing to differentiate lymph node metastasis in WSIs., Results: The proposed model achieves the highest accuracy of 95.3% and 99.8%, precision values of 98% and 99.8%, and recall rates of 92.9% and 99.8% on the Camelyon16 and Camelyon17 datasets., Conclusions: These findings underscore the effectiveness of ImDeTraC-BCNet-MIL in enhancing the early detection of lymph node metastasis in breast cancer., (© 2024 John Wiley & Sons Ltd.)

Introduction: Cancer is a leading cause of death worldwide, necessitating effective diagnostic tools for early detection and treatment. Histopathological image analysis is crucial for cancer diagnosis but is often hindered by human error and variability. This study introduces HistopathAI, a hybrid network designed for histopathology image classification, aimed at enhancing diagnostic precision and efficiency in clinical pathology., Objectives: The primary goal of this study is to demonstrate that HistopathAI, leveraging supervised contrastive learning (SCL) and hybrid deep feature fusion (HDFF), can significantly improve the accuracy of histopathological image classification, including scenarios involving imbalanced datasets., Methods: HistopathAI integrates features from EfficientNetB3 and ResNet50, using HDFF to provide a rich representation of histopathology images. The framework employs a sequential methodology, transitioning from feature learning to classifier learning, mirroring the essence of contrastive learning with the aim of producing superior feature representations. The model combines SCL for feature representation with cross-entropy (CE) loss for classification. We evaluated HistopathAI across seven publicly available datasets and one private dataset, covering various histopathology domains., Results: HistopathAI achieved state-of-the-art classification accuracy across all datasets, demonstrating superior performance in both binary and multiclass classification tasks. Statistical testing confirmed that HistopathAI's performance is significantly better than baseline models, ensuring robust and reliable improvements., Conclusion: HistopathAI offers a robust tool for histopathology image classification, enhancing diagnostic accuracy and supporting the transition to digital pathology. This framework has the potential to improve cancer diagnosis and patient outcomes, paving the way for broader clinical application. The code is available on https://github.com/Mamunur-20/HistopathAI., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Convolution neural networks (CNN) have shown the state of the art performance for visual recognition, classification of images, time series, and sequential data. Despite good performance, it has few serious drawbacks that it cannot properly encode the orientation and spatial positioning of components of the input data and it also suffers from overfitting. Quaternion CNN is a generalization of traditional CNN and it can properly encode internal and external dependencies between the components of the input data and it is free from overfitting and its generalization performance is better than conventional CNN. It can be modified to work with all Deep Neural Network (DNN) models with quaternion as input. In this paper, the authors have proposed quaternion residual learning for the classification of breast cancer in the BreakHis dataset of breast histopathological images. They have observed that quaternion CNN outperforms to real CNN. The experiment has obtained the classification accuracy of 98.04% and F-score of 0.9842. [ABSTRACT FROM AUTHOR]

Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.

Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers. [ABSTRACT FROM AUTHOR]

The presence of necrosis is associated with tumor progression and patient outcomes in many cancers, but existing analyses rarely adopt quantitative methods because the manual quantification of histopathological features is too expensive. We aim to accurately identify necrotic regions on hematoxylin and eosin (HE)–stained slides and to calculate the ratio of necrosis with minimal annotations on the images. An adaptive method named Learning from Label Fuzzy Proportions (LLFP) was introduced to histopathological image analysis. Two datasets of liver cancer HE slides were collected to verify the feasibility of the method by training on the internal set using cross validation and performing validation on the external set, along with ensemble learning to improve performance. The models from cross validation performed relatively stably in identifying necrosis, with a Concordance Index of the Slide Necrosis Score (CISNS) of 0.9165±0.0089 in the internal test set. The integration model improved the CISNS to 0.9341 and achieved a CISNS of 0.8278 on the external set. There were significant differences in survival (p = 0.0060) between the three groups divided according to the calculated necrosis ratio. The proposed method can build an integration model good at distinguishing necrosis and capable of clinical assistance as an automatic tool to stratify patients with different risks or as a cluster tool for the quantification of histopathological features. We presented a method effective for identifying histopathological features and suggested that the extent of necrosis, especially micronecrosis, in liver cancer is related to patient outcomes. [ABSTRACT FROM AUTHOR]

An efficient classification method to categorize histopathological images is a challenging research problem. In this paper, an improved bag-of-features approach is presented as an efficient image classification method. In bag-of-features, a large number of keypoints are extracted from histopathological images that increases the computational cost of the codebook construction step. Therefore, to select the a relevant subset of keypoints, a new keypoints selection method is introduced in the bag-of-features method. To validate the performance of the proposed method, an extensive experimental analysis is conducted on two standard histopathological image datasets, namely ADL and Blue histology datasets. The proposed keypoint selection method reduces the extracted high dimensional features by 95% and 68% from the ADL and Blue histology datasets respectively with less computational time. Moreover, the enhanced bag-of-features method increases classification accuracy by from other considered classification methods. [ABSTRACT FROM AUTHOR]

Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed based on the data from multiple medical centers. This requirement makes it a challenging task to normalize the color variance of histopathological images from different medical centers. In this paper, we propose a novel color standardization module named stain standardization capsule based on the capsule network and the corresponding dynamic routing algorithm. The proposed module can learn and generate uniform stain separation outputs for histopathological images in various color appearance without the reference to manually selected template images. The proposed module is light and can be jointly trained with the application-driven CNN model. The proposed method was validated on three histopathology datasets and a cytology dataset, and was compared with state-of-the-art methods. The experimental results have demonstrated that the SSC module is effective in improving the performance of histopathological image analysis and has achieved the best performance in the compared methods. [ABSTRACT FROM AUTHOR]

Histopathological image analysis is a challenging task due to a diverse histology feature set as well as due to the presence of large non-informative regions in whole slide images. In this paper, we propose a multiple-instance learning (MIL) method for image-level classification as well as for annotating relevant regions in the image. In MIL, a common assumption is that negative bags contain only negative instances while positive bags contain one or more positive instances. This asymmetric assumption may be inappropriate for some application scenarios where negative bags also contain representative negative instances. We introduce a novel symmetric MIL framework associating each instance in a bag with an attribute which can be either negative, positive, or irrelevant. We extend the notion of relevance by introducing control over the number of relevant instances. We develop a probabilistic graphical model that incorporates the aforementioned paradigm and a corresponding computationally efficient inference for learning the model parameters and obtaining an instance level attribute-learning classifier. The effectiveness of the proposed method is evaluated on available histopathology datasets with promising results. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common type of malignancy diagnosed in women. Through early detection and diagnosis, there is a great chance of recovery and thereby reduce the mortality rate. Many preliminary tests like non-invasive radiological diagnosis using ultrasound, mammography, and MRI are widely used for the diagnosis of breast cancer. However, histopathological analysis of breast biopsy specimen is inevitable and is considered to be the golden standard for the affirmation of cancer. With the advancements in the digital computing capabilities, memory capacity, and imaging modalities, the development of computer-aided powerful analytical techniques for histopathological data has increased dramatically. These automated techniques help to alleviate the laborious work of the pathologist and to improve the reproducibility and reliability of the interpretation. This paper reviews and summarizes digital image computational algorithms applied on histopathological breast cancer images for nuclear atypia scoring and explores the future possibilities. The algorithms for nuclear pleomorphism scoring of breast cancer can be widely grouped into two categories: handcrafted feature-based and learned feature-based. Handcrafted feature-based algorithms mainly include the computational steps like pre-processing the images, segmenting the nuclei, extracting unique features, feature selection, and machine learning–based classification. However, most of the recent algorithms are based on learned features, that extract high-level abstractions directly from the histopathological images utilizing deep learning techniques. In this paper, we discuss the various algorithms applied for the nuclear pleomorphism scoring of breast cancer, discourse the challenges to be dealt with, and outline the importance of benchmark datasets. A comparative analysis of some prominent works on breast cancer nuclear atypia scoring is done using a benchmark dataset which enables to quantitatively measure and compare the different features and algorithms used for breast cancer grading. Results show that improvements are still required, to have an automated cancer grading system suitable for clinical applications. [ABSTRACT FROM AUTHOR]

Color variation in stained histopathology images prevents the development of computer-assisted diagnosis (CAD) algorithms for whole slide imaging systems. Therefore, stain normalization methods are studied to reduce the influence of color variation combined with digital image processing algorithms. The Structure Preserve Color Normalization (SPCN) method is a promising stain normalization method, utilizing the sparse non-negative matrix factorization to estimate the stain feature appearance matrix. However, the SPCN method suffers from the high computational complexity of dictionary learning, and its official implementation relies on Matlab and CPU. This research proposes the StainCNNs method to simplify the process of stain feature extraction, and imply a GPU-enabled realization to accelerate the learning of stain features in the Tensorflow Framework. What's more, the StainCNNs method is able to perform the stain normalization quickly in dataset level, more efficient than the SPCN method which is unable to make use of the stain feature distribution in dataset. Stain normalization experiments are conducted on the Camelyon16 dataset and the ICPR2014 dataset, evaluated by the QSSIM score and the FSIM score. Results demonstrate that the proposed StainCNNs method achieves a state-of-the-art performance compared with many conventional stain normalization methods. [ABSTRACT FROM AUTHOR]

• We first apply deep metric learning to histopathological image retrieval tasks. • The proposed method derives semantically reasonable similarity metric for histopathological images. • The proposed method achieves the best retrieval results on the public dataset Kimia Path24. • The proposed method outperforms the attention-based ensemble model with less computation consumption. • The proposed method works well with limited training data. To distinguish ambiguous images during specimen slides viewing, pathologists usually spend lots of time to seek guidance from confirmed similar images or cases, which is inefficient. Therefore, several histopathological image retrieval methods have been proposed for pathologists to easily obtain images sharing similar content with the query images. However, these methods cannot ensure a reasonable similarity metric, and some of them need lots of annotated images to train a feature extractor to represent images. Motivated by this circumstance, we propose the first deep metric learning-based histopathological image retrieval method in this paper and construct a deep neural network based on the mixed attention mechanism to learn an embedding function under the supervision of image category information. With the learned embedding function, original images are mapped into the predefined metric space where similar images from the same category are close to each other, so that the distance between image pairs in the metric space can be regarded as a reasonable metric for image similarity. We evaluate the proposed method on two histopathological image retrieval datasets: our self-established dataset and a public dataset called Kimia Path24, on which the proposed method achieves recall in top-1 recommendation (Recall @ 1) of 84.04% and 97.89% respectively. Moreover, further experiments confirm that the proposed method can achieve comparable performance to several published methods with less training data, which hedges the shortage of annotated medical image data to some extent. Code is available at https://github.com/easonyang1996/DML_HistoImgRetrieval. [ABSTRACT FROM AUTHOR]

Liver cancer is a leading cause of cancer deaths worldwide due to its high morbidity and mortality. Histopathological image analysis (HIA) is a crucial step in the early diagnosis of liver cancer and is routinely performed manually. However, this process is time-consuming, error-prone, and easily affected by the expertise of pathologists. Recently, computer-aided methods have been widely applied to medical image analysis; however, the current medical image analysis studies have not yet focused on the histopathological morphology of liver cancer due to its complex features and the insufficiency of training images with detailed annotations. This paper proposes a deep learning method for liver cancer histopathological image classification using only global labels. To compensate for the lack of detailed cancer region annotations in those images, patch features are extracted and fully utilized. Transfer learning is used to obtain the patch-level features and then combined with multiple-instance learning to acquire the image-level features for classification. The method proposed here solves the processing of large-scale images and training sample insufficiency in liver cancer histopathological images for image classification. The proposed method can distinguish and classify liver histopathological images as abnormal or normal with high accuracy, thus providing support for the early diagnosis of liver cancer. [ABSTRACT FROM AUTHOR]

In this study, we analyze histologic human colon tissue images that we captured with a camera-mounted microscope. We propose the Augmented K-Means Clustering algorithm as a method of segmenting cell nuclei in such colon images. Then we compare the proposed algorithm to the weighted K-Means Clustering algorithm. As a result, we observe that the developed Augmented K-Means Clustering algorithm decreased the needed number of iterations and shortened the duration of the segmentation process. Moreover, the algorithm we propose appears more consistent in comparison to the weighted K-Means Clustering algorithm. We also assess the similarity of the segmented images to the original images, for which we used the Histogram-Based Similarity method. Our assessment indicates that the images segmented by the Augmented K-Means Clustering algorithm are more frequently similar to the original images than the images segmented by the Weighed K-Means Clustering algorithm.

Highlights • Fully automated technique for lymph node segmentation & PI Calculation. • Technique is robust to several stains: H&E, MART-1, S-100, KI-67. • More than 90% segmentation accuracy is achieved. • Automatically calculated PI values close match with Ground truth. • An important step for automated melanoma grading. Abstract The lymphatic system is the immune system of the human body, and includes networks of vessels spread over the body, lymph nodes, and lymph fluid. The lymph nodes are considered as purification units that collect the lymph fluid from the lymph vessels. Since the lymph nodes collect the cancer cells that escape from a malignant tumor and try to spread to the rest of the body, the lymph node analysis is important for staging many types skin and breast cancers. In this paper, we propose a Computer Aided Diagnosis (CAD) method that segments the lymph nodes and melanoma regions in a biopsy image and measure the proliferation index. The proposed method contains two stages. First, an automated technique is used to segment the lymph nodes in a biopsy image based on histogram and high frequency features. In the second stage, the proliferation index for the melanoma regions is calculated by comparing the number of active and passive nuclei. Experimental results on 76 different lymph node images show that the proposed segmentation technique can robustly segment the lymph nodes with more than 90% accuracy. The proposed proliferation index calculation has low complexity and has an average error rate of less than 1.5%. [ABSTRACT FROM AUTHOR]

Grading intraepithelial neoplasia is crucial to derive an accurate estimate of pre-cancerous stages and is currently performed by pathologists assessing histopathological images. Inter- and intra-observer variability can significantly be reduced, when reliable, quantitative image analysis is introduced into diagnostic processes. On a challenging dataset, we evaluated the potential of learning a classifier to grade anal intraepitelial neoplasia. Support vector machines were trained on images represented by fractal and statistical features. We show that pursuing a learning-based grading strategy yields highly reliable results. Compared to existing methods, the proposed method outperformed them by a significant margin.

The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

The analysis of cell characteristics from high-resolution digital histopathological images is the standard clinical practice for the diagnosis and prognosis of cancer. Yet, it is a rather exhausting process for pathologists to examine the cellular structures manually in this way. Automating this tedious and time-consuming process is an emerging topic of the histopathological image-processing studies in the literature. This paper presents a two-stage segmentation method to obtain cellular structures in high-dimensional histopathological images of renal cell carcinoma. First, the image is segmented to superpixels with simple linear iterative clustering (SLIC) method. Then, the obtained superpixels are clustered by the state-of-the-art clustering-based segmentation algorithms to find similar superpixels that compose the cell nuclei. Furthermore, the comparison of the global clustering-based segmentation methods and local region-based superpixel segmentation algorithms are also compared. The results show that the use of the superpixel segmentation algorithm as a pre-segmentation method improves the performance of the cell segmentation as compared to the simple single clustering-based segmentation algorithm. The true positive ratio (TPR), true negative ratio (TNR), F-measure, precision, and overlap ratio (OR) measures are utilized as segmentation performance evaluation. The computation times of the algorithms are also evaluated and presented in the study. Graphical Abstract The visual flowchart of the proposed automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. [ABSTRACT FROM AUTHOR]

Summary: Recently, the deep learning methods have received more attention in histopathological image analysis. However, the traditional deep learning methods assume that training data and test data have the same distributions, which causes certain limitations in real‐world histopathological applications. However, it is costly to recollect a large amount of labeled histology data to train a new neural network for each specified image acquisition procedure even for similar tasks. In this paper, an unsupervised domain adaptation is introduced into a typical deep convolutional neural network (CNN) model to mitigate the repeating of the labels. The unsupervised domain adaptation is implemented by adding two regularisation terms, namely the feature‐based adaptation and entropy minimisation, to the object function of a widely used CNN model called the AlexNet. Three independent public epithelium‐stroma datasets were used to verify the proposed method. The experimental results have demonstrated that in the epithelium‐stroma classification, the proposed method can achieve better performance than the commonly used deep learning methods and some existing deep domain adaptation methods. Therefore, the proposed method can be considered as a better option for the real‐world applications of histopathological image analysis because there is no requirement for recollection of large‐scale labeled data for every specified domain. [ABSTRACT FROM AUTHOR]

Background There are few reports comparing type 1 alveolar epithelial cell development with histopathological image analysis. We investigated these as indicators of maturity in fetal lambs' lungs in a congenital diaphragmatic hernia (CDH) model. Methods We created left CDH in 4 fetal lambs at 75 or 76 days’ gestation (Group A). Controls were 5 sham-operated lambs (Group B); both groups delivered at term. The right lower lung lobe (RLL) and left lower lobe (LLL) were sampled. Using histopathological image analysis, alveoli/air sacs count (AC), alveoli/air sacs area percentage (AP), average area (AA), total area (TA), and perimeter (PM) were determined. We also evaluated total lung volumes, radial alveolar count (RAC), and Type 1 alveolar epithelial cells ratio (AT1 ratio), which we previously reported. Regression analysis was performed, with p < 0.05 considered significant. Results RLL and LLL AT1 ratio and LLL RAC in Group A were lower than in Group B. There are no significant differences demonstrated by histopathological image analysis. In Group A, the AT1 ratio in the LLL was lower than in the RLL. There were no differences between LLL and RLL in Group B. Conclusion AT1 ratio was superior to the other indicators evaluating lung maturity. [ABSTRACT FROM AUTHOR]

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. [ABSTRACT FROM AUTHOR]

Segmenting a broad class of histological structures in transmitted light and/or fluorescence-based images is a prerequisite for determining the pathological basis of cancer, elucidating spatial interactions between histological structures in tumor microenvironments (e.g., tumor infiltrating lymphocytes), facilitating precision medicine studies with deep molecular profiling, and providing an exploratory tool for pathologists. This paper focuses on segmenting histological structures in hematoxylin- and eosin-stained images of breast tissues, e.g., invasive carcinoma, carcinoma in situ, atypical and normal ducts, adipose tissue, and lymphocytes. We propose two graph-theoretic segmentation methods based on local spatial color and nuclei neighborhood statistics. For benchmarking, we curated a data set of 232 high-power field breast tissue images together with expertly annotated ground truth. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.) over the remaining connective tissue and non-tissue areas in ground truth annotations, we propose a new region-based score for evaluating segmentation algorithms. We demonstrate the improvement of our proposed methods over the state-of-the-art algorithms in both region- and boundary-based performance measures. [ABSTRACT FROM PUBLISHER]

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data. [ABSTRACT FROM AUTHOR]

Gene mutation detection is usually carried out by molecular biological methods, which is expensive and has a long-time cycle. In contrast, pathological images are ubiquitous. If clinically significant gene mutations can be predicted only through pathological images, it will greatly promote the widespread use of gene mutation detection in clinical practice. However, current gene mutation prediction methods based on pathological images are ineffective because of the inability to identify mutated regions in gigapixel Whole Slide Image (WSI). To address this challenge, hereby we propose a carefully designed framework for WSI-based gene mutation prediction, which consists of three parts. (i) The first part of cancerous area segmentation, based on supervised learning, quickly filters out a large number of non-mutated regions; (ii) the second part of cancerous patch clustering, based on the representations derived from contrastive learning, ensures the comprehensiveness of patch selection; and (iii) the third part of mutation classification, based on the proposed hierarchical deep multi-instance learning method (HDMIL), ensures that sufficient patches are considered and inaccurate selections are ignored. In addition, benefiting from a two-stage attention mechanism in HDMIL, the patches that are highly correlated with gene mutations can be identified. This interpretability can help a pathologist to analyze the correlation between gene mutation and histopathological morphology. Experimental results demonstrate that the proposed gene mutation prediction framework significantly outperforms the state-of-the-art methods. In the TCGA bladder cancer dataset, five clinically relevant gene mutations are well predicted. • A novel gene mutation prediction framework considering representative patches in WSI. • The patches that are highly correlated with a gene mutation can be identified. • Five clinically relevant gene mutations in bladder cancer can be well predicted. [ABSTRACT FROM AUTHOR]