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Interstitial pneumonia is a heterogeneous disease with a progressive course and poor prognosis, at times even worse than those in the main cancer types. Histopathological examination is crucial for its diagnosis and estimation of prognosis. However, the evaluation strongly depends on the experience of pathologists, and the reproducibility of diagnosis is low.Herein, we propose MIXTURE (huMan-In-the-loop eXplainable artificial intelligence Through the Use of REcurrent training), a method to develop deep learning models for extracting pathologically significant findings based on an expert pathologist’s perspective with a small annotation effort. The procedure of MIXTURE consists of three steps as follows. First, we created feature extractors for tiles from whole slide images using self-supervised learning. The similar looking tiles were clustered based on the output features and then pathologists integrated the pathologically synonymous clusters. Using the integrated clusters as labeled data, deep learning models to classify the tiles into pathological findings were created by transfer-learning the feature extractors. We developed three models for different magnifications.Using these extracted findings, our model was able to predict the diagnosis of usual interstitial pneumonia, a finding suggestive of progressive disease, with high accuracy (AUC 0.90). This high accuracy could not be achieved without the integration of findings by pathologists. The patients predicted as UIP had significantly poorer prognosis (five-year overall survival [OS]: 55.4%) than those predicted as non-UIP (OS: 95.2%). The Cox proportional hazards model for each microscopic finding and prognosis pointed out dense fibrosis, fibroblastic foci, elastosis, and lymphocyte aggregation as independent risk factors. We suggest that MIXTURE may serve as a model approach to different diseases evaluated by medical imaging, including pathology and radiology, and be the prototype for artificial intelligence that can collaborate with humans.

The emergence of whole slide imaging technology allows for pathology diagnosis on a computer screen. The applications of digital pathology are expanding, from supporting remote institutes suffering from a shortage of pathologists to routine use in daily diagnosis including that of lung cancer. Through practice and research large archival databases of digital pathology images have been developed that will facilitate the development of artificial intelligence (AI) methods for image analysis. Currently, several AI applications have been reported in the field of lung cancer; these include the segmentation of carcinoma foci, detection of lymph node metastasis, counting of tumor cells, and prediction of gene mutations. Although the integration of AI algorithms into clinical practice remains a significant challenge, we have implemented tumor cell count for genetic analysis, a helpful application for routine use. Our experience suggests that pathologists often overestimate the contents of tumor cells, and the use of AI-based analysis increases the accuracy and makes the tasks less tedious. However, there are several difficulties encountered in the practical use of AI in clinical diagnosis. These include the lack of sufficient annotated data for the development and validation of AI systems, the explainability of black box AI models, such as those based on deep learning that offer the most promising performance, and the difficulty in defining the ground truth data for training and validation owing to inherent ambiguity in most applications. All of these together present significant challenges in the development and clinical translation of AI methods in the practice of pathology. Additional research on these problems will help in resolving the barriers to the clinical use of AI. Helping pathologists in developing knowledge of the working and limitations of AI will benefit the use of AI in both diagnostics and research.

INTRODUCTION AND OBJECTIVE:Cystoscopy is the primary diagnostic modality for bladder cancer. However, the level of experience of urologists may affect the accuracy of the diagnosis. We developed an...

Deep learning using deep convolutional neural networks (DCNNs) has demonstrated unprecedented power in image classification. Subsequently, computer-aided diagnosis (CAD) for pathology imaging has been largely facilitated by DCNN approaches. However, because DCNNs require massive amounts of labeled data, the lack of availability of such pathology image data as well as the high cost of labeling new data is currently a major problem. To avoid expensive data labeling efforts, transfer learning is a concept intended to overcome training data shortages by passing knowledge from the source domain to the target domain. However, weak relevance between the source and target domains generally leads to less effective transfers. Therefore, following the natural step-by-step process by which humans learn and make inferences, a stepwise fine-tuning scheme is proposed by introducing intermediate domains to bridge the source and target domains. The DCNNs are expected to acquire general object classification knowledge from a source domain dataset such as ImageNet and pathology-related knowledge from intermediate domain data, which serve as fundamental and specific knowledge, respectively, for the final benign/malignant classification task. To realize this, we introduce several ways to provide pathology-related knowledge by generating an intermediate dataset classified into various corresponding pathology features. In experiments, the proposed scheme has demonstrated good performance on several well-known deep neural networks.

Introduction: Nonmuscle-invasive bladder cancer has a relatively high postoperative recurrence rate despite the implementation of conventional treatment methods. Cystoscopy is essential for diagnosing and monitoring bladder cancer, but lesions are overlooked while using white-light imaging. Using cystoscopy, tumors with a small diameter; flat tumors, such as carcinoma in situ; and the extent of flat lesions associated with the elevated lesions are difficult to identify. In addition, the accuracy of diagnosis and treatment using cystoscopy varies according to the skill and experience of physicians. Therefore, to improve the quality of bladder cancer diagnosis, we aimed to support the cystoscopic diagnosis of bladder cancer using artificial intelligence (AI). Materials and Methods: A total of 2102 cystoscopic images, consisting of 1671 images of normal tissue and 431 images of tumor lesions, were used to create a dataset with an 8:2 ratio of training and test images. We constructed a tumor classifier based on a convolutional neural network (CNN). The performance of the trained classifier was evaluated using test data. True-positive rate and false-positive rate were plotted when the threshold was changed as the receiver operating characteristic (ROC) curve. Results: In the test data (tumor image: 87, normal image: 335), 78 images were true positive, 315 true negative, 20 false positive, and 9 false negative. The area under the ROC curve was 0.98, with a maximum Youden index of 0.837, sensitivity of 89.7%, and specificity of 94.0%. Conclusion: By objectively evaluating the cystoscopic image with CNN, it was possible to classify the image, including tumor lesions and normality. The objective evaluation of cystoscopic images using AI is expected to contribute to improvement in the accuracy of the diagnosis and treatment of bladder cancer.

Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases, such as ulcerative colitis. Thus, to assist the UC diagnosis, it is necessary to develop a new technology that can retrieve similar cases of diagnostic target image from cases in the past that stored the diagnosed images with various symptoms of colonic mucosa. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multiscale objects. The proposed method can retrieve similar colonoscopy images despite varying visible sizes of the target objects. Through three experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any visible size and any location at a high level of accuracy.

This paper presents a method of segmenting mammary gland tissue for breast ultrasound images. In order to improve the quality of diagnoses for breast ultrasound screening, Computer Aided Detection (CADe) systems have been developed by a number of researchers. However, such systems suffer in terms of making many false positives for non-mammary gland tissue where no cancer exists. Accordingly, it is necessary to accurately determine the area of mammary gland tissue within ultrasound images for CADe systems. In this study, we propose a method of segmenting mammary gland tissue from ultrasound images for breasts. Within ultrasound images, brightness is attenuated as ultrasound waves move from the ultrasound probe to internal tissue. Accordingly, we correct intensity (brightness) using zero-phase component analysis (ZCA) whitening in order to reduce such brightness attenuation. Moreover, we apply a Conditional Random Field (CRF) which can render as smooth the region boundary of ultrasound images. The results of an experiment, conducted with five target patients to verify the effectiveness of the proposed methods, demonstrate that the method is capable of segmenting mammary gland tissue from breast ultrasound images.

This paper proposes a method of retrieving multi-scale objects from optical colonoscopy images based on image-recognition techniques. Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical-colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multi-scale objects. The proposed method can retrieve similar images despite varying sizes of the target objects. Through experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any size at high levels of accuracy.

This paper proposes a content-based image retrieval method for optical colonoscopy images that can find images similar to ones being diagnosed. Optical colonoscopy is a method of direct observation for colons and rectums to diagnose bowel diseases. It is the most common procedure for screening, surveillance and treatment. However, diagnostic accuracy for intractable inflammatory bowel diseases, such as ulcerative colitis (UC), is highly dependent on the experience and knowledge of the medical doctor, because there is considerable variety in the appearances of colonic mucosa within inflammations with UC. In order to solve this issue, this paper proposes a content-based image retrieval method based on image recognition techniques. The proposed retrieval method can find similar images from a database of images diagnosed as UC, and can potentially furnish the medical records associated with the retrieved images to assist the UC diagnosis. Within the proposed method, color histogram features and higher order local auto-correlation (HLAC) features are adopted to represent the color information and geometrical information of optical colonoscopy images, respectively. Moreover, considering various characteristics of UC colonoscopy images, such as vascular patterns and the roughness of the colonic mucosa, we also propose an image enhancement method to highlight the appearances of colonic mucosa in UC. In an experiment using 161 UC images from 32 patients, we demonstrate that our method improves the accuracy of retrieving similar UC images.

Capsule endoscopy is a patient-friendly endoscopy broadly utilized in gastrointestinal examination. However, the efficacy of diagnosis is restricted by the large quantity of images. This paper presents a modified anomaly detection method, by which both known and unknown anomalies in capsule endoscopy images of small intestine are expected to be detected. To achieve this goal, this paper introduces feature extraction using a non-linear color conversion and Higher-order Local Auto Correlation (HLAC) Features, and makes use of image partition and subspace method for anomaly detection. Experiments are implemented among several major anomalies with combinations of proposed techniques. As the result, the proposed method achieved 91.7% and 100% detection accuracy for swelling and bleeding respectively, so that the effectiveness of proposed method is demonstrated.

Capsule endoscopy is a painless way more and more utilized in gastrointestinal examination. Nevertheless, there is an issue comes out that the efficiency and accuracy of capsule endoscopy diagnosis is now restricted by the large quantity of images. In this paper, an anomaly detection method for capsule endoscopy images captured within the range of small intestine is described. Aiming to realize the anomaly detection, this paper takes the advantage of Higher-order Local Auto Correlation features and subspace method using PCA (Principal Component Analysis). The proposed method is validated over capsule endoscopy image sets and its effectiveness is demonstrated by experimental results.

This paper proposes a technology for histopathological diagnostic support that utilizes the correlation-based features of histopathological tissues. In histopathological diagnosis, a clinical pathologist conducts a diagnosis of normal tissues and cancerous tissues. However, recently, the shortage of clinical pathologists is posing increasing burdens to meet the demands for such diagnoses, and this is causing serious social problems. In order to overcome this problem, we propose a technology of histopathological diagnostic support that uses higher-order local autocorrelation (HLAC) features. The proposed method can automatically screen tissue that is believed to be normal tissue to detect cancerous tissue as well as tissue that is suspected of being cancerous to detect abnormalities. Consequently, we can reduce the burden on clinical pathologists, allowing them to concentrate on diagnosing cancer.

Pathological diagnosis, plays a very crucial role in cancer examination. However, because of the shortage of pathologists, increasing working burden has been imposed on pathologists. Aiming to reduce pathologist's burden and improve the reliability of cancer diagnosis, an innovative cancer detection method for pathological images and its extended approach for advanced ability is presented in this paper. The experiments demonstrate the effectiveness of the proposed method in cancer detection and cancer location estimation.