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[Display omitted] • Introduce capsule network for multi-focus image fusion. • Utilize structure information to help locate focus regions. • Design structure-guided flow module to integrate structure features. Existing deep learning based methods have shown their advantages in multi-focus image fusion task. However, most methods still suffer from inaccurate focus region detection. In this paper, we employ the property of part-whole relationships embedded by the Capsule Network (CapsNet) to solve the problem. Specifically, we introduce CapsNet in multi-focus image fusion task, and design a structure-guided flow module, which fully utilizes structure information to help locate focus regions. CapsNet is introduced to extract structure features by supervising gradient information of the image. Compared with traditional convolutional neural networks (CNNs), CapsNet takes into account the correlation of features from different positions, such that it encodes more compact features. Once structure features are obtained, a flow alignment module is introduced to learn flow field between structure and image features, and propagate effectively structure features to image features to make confident focus region detection. Experimental results show the proposed method achieves robust fusion performance on three publicly available multi-focus datasets, and outperforms or is comparable to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Our MTDD model is an integrated knowledge-driven and data-driven model. This method avoids problems such as not considering expert experience or insight, not paying attention to the overall situation, and lack of interpretability brought about by only using data-driven technology. It not only utilizes the text features and the semantic features but also applies the domain knowledge to learn the representation of the depression tendency, thus making the model more robust. In other words, the model combines text features, semantic features, and domain knowledge. The Word2Vec word embedding integrates the emotional information of the words in the emotional dictionary, expands the existing emotional dictionary, extracts the TF-IDF word frequency feature, and proposes seven grammatical analysis rules to obtain the text emotional value feature making it more suitable for depression tendency detection classification task. • The MTDD model is a deep neural network hybrid model, which circumvents the weak generalization ability of a single model for identifying depression tendencies. Specifically, the MTDD model combines the advantages of CNN and Bi-LSTM networks. The CNN network offers the advantage of the ability to extract the local features of the text. In addition, BiLSTM can effectively capture bidirectional semantic information. This combination can better represent text features and improve the model's classification accuracy. • Our MTDD model is obtained based on real data, thus making the model more suitable for practical problems. As far as we know, many existing depression detection methods are only trained on some experimental data sets, so the model's generalization ability is limited and cannot even be applied in realistic scenes. In comparison, the MTDD model is trained on social platform data, making the data more objective and accurate. In addition, the data of social platforms can be obtained at a low cost. It is easy to operate and does not require a lot of laborious labeling. Moreover, our approach avoids the influence of subjective factors in the method of consultation by mental health experts and the influence of non-public and imperfect data used for depression. • We conducted extensive experiments on a Reddit data set and a Twitter data set. The results show that, compared to multiple latest depression detection models, our MTDD model detects users who may be depressed with a 95% F1 value and obtains SOTA results. [Display omitted] Background and Objective: Depression can severely impact physical and mental health and may even harm society. Therefore, detecting the early symptoms of depression and treating them on time is critical. The widespread use of social media has led individuals with depressive tendencies to express their emotions on social platforms, share their painful experiences, and seek support and help. Therefore, the massive available amounts of social platform data provide the possibility of identifying depressive tendencies. Methods: This paper proposes a neural network hybrid model MTDD to achieve this goal. Analysis of the content of users' posts on social platforms has facilitated constructing a post-level method to detect depressive tendencies in individuals. Compared with existing methods, the MTDD model uses the following innovative methods: First, this model is based on social platform data, which is objective and accurate, can be obtained at a low cost, and is easy to operate. The model can avoid the influence of subjective factors in the depressive tendency detection method based on consultation with mental health experts. In other words, it can avoid the problem of undisclosed and imperfect data in depressive tendency detection. Second, the MTDD model is based on a deep neural network hybrid model, combining the advantages of CNN and BiLSTM networks and avoiding the problem of poor generalization ability in a single model for depression tendency recognition. Third, the MTDD model is based on multimodal features for learning the vector representation of depression-prone text, including text features, semantic features, and domain knowledge, making the model more robust. Results: Extensive experimental results demonstrate that our MTDD model detects users who may have a depressive tendency with a 95 % F1 value and obtained SOTA results. Conclusions: Our MTDD model can detect depressive users on social media platforms more effectively, providing the possibility for early diagnosis and timely treatment of depression. The experiment proves that our MTDD model outperforms many of the latest depressive tendency detection models. [ABSTRACT FROM AUTHOR]

• We present ChampKit, a Python-based software package that enables rapid exploration and evaluation of deep learning models for patch-level classification of histopathology data. It is open source and available at https://github.com/SBU-BMI/champkit. ChampKit is designed to be highly reproducible and enables systematic, unbiased evaluation of patch-level histopathology classification. It incorporates public datasets for six clinically important tasks and access to hundreds of (pre-trained) deep learning models. It can easily be extended to custom patch classification datasets and custom deep learning architectures. • The users are intended to be (1) biomedical research groups interested in finding and fine-tuning the best models to analyze a broad collection of whole slide images, and (2) deep learning methods research groups interested in systematically and quickly evaluating their methods against a set of state-of-the-art methods with different pretraining and transfer learning configurations. • We demonstrate the utility of ChampKit by evaluating two ResNet models and one vision transformer on the six diverse classification tasks for patch-level histopathology datasets. We did not find consistent benefits from pretrained models versus random initialization across the different datasets, which suggests that a thorough exploration of model architectures is important to identify optimal models for a given dataset. Histopathology is the gold standard for diagnosis of many cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for many tasks, including the detection of immune cells and microsatellite instability. However, it remains difficult to identify optimal models and training configurations for different histopathology classification tasks due to the abundance of available architectures and the lack of systematic evaluations. Our objective in this work is to present a software tool that addresses this need and enables robust, systematic evaluation of neural network models for patch classification in histology in a light-weight, easy-to-use package for both algorithm developers and biomedical researchers. Here we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible evaluation toolkit that is a one-stop-shop to train and evaluate deep neural networks for patch classification. ChampKit curates a broad range of public datasets. It enables training and evaluation of models supported by timm directly from the command line, without the need for users to write any code. External models are enabled through a straightforward API and minimal coding. As a result, Champkit facilitates the evaluation of existing and new models and deep learning architectures on pathology datasets, making it more accessible to the broader scientific community. To demonstrate the utility of ChampKit, we establish baseline performance for a subset of possible models that could be employed with ChampKit, focusing on several popular deep learning models, namely ResNet18, ResNet50, and R26-ViT, a hybrid vision transformer. In addition, we compare each model trained either from random weight initialization or with transfer learning from ImageNet pretrained models. For ResNet18, we also consider transfer learning from a self-supervised pretrained model. The main result of this paper is the ChampKit software. Using ChampKit, we were able to systemically evaluate multiple neural networks across six datasets. We observed mixed results when evaluating the benefits of pretraining versus random intialization, with no clear benefit except in the low data regime, where transfer learning was found to be beneficial. Surprisingly, we found that transfer learning from self-supervised weights rarely improved performance, which is counter to other areas of computer vision. Choosing the right model for a given digital pathology dataset is nontrivial. ChampKit provides a valuable tool to fill this gap by enabling the evaluation of hundreds of existing (or user-defined) deep learning models across a variety of pathology tasks. Source code and data for the tool are freely accessible at https://github.com/SBU-BMI/champkit. [ABSTRACT FROM AUTHOR]