Neural network interpretation techniques for analysis of histological images of breast abnormalities

Background. Neural networks are actively used in digital pathology to analyze histological images and support medical decision-making. A common approach is to solve the classification problem, where only class labels are the only model responses. However, one should understand which areas of the image have the most significant impact on the model's response. Machine learning interpretation techniques help solve this problem. Aim. To study the consistency of different methods of neural network interpretation when classifying histological images of the breast and to obtain an expert assessment of the results of the evaluated methods. Materials and methods. We performed a preliminary analysis and pre-processing of the existing data set used to train pre-selected neural network models. The existing methods of visualizing the areas of attention of trained models on easy-to-understand data were applied, followed by verification of their correct use. The same neural network models were trained on histological data, and the selected interpretation methods were used to systematize histological images, followed by the evaluation of the results consistency and an expert assessment of the results. Results. In this paper, several methods of interpreting machine learning are studied using two different neural network architectures and a set of histological images of breast abnormalities. Results of ResNet18 and ViT-B-16 models training on a set of histological images on the test sample: accuracy metric 0.89 and 0.89, ROC_AUC metric 0.99 and 0.96, respectively. The results were also evaluated by an expert using the Label Studio tool. For each pair of images, the expert was asked to select the most appropriate answer ("Yes" or "No") to the question: "The highlighted areas generally correspond to the Malignant class." The "Yes" response rate for the ResNet_Malignant category was 0.56; for ViT_Malignant, it was 1.0. Conclusion. Interpretability experiments were conducted with two different architectures: the ResNet18 convolutional network and the ViT-B-16 attention-enhanced network. The results of the trained models were visualized using the GradCAM and Attention Rollout methods, respectively. First, experiments were conducted on a simple-to-interpret dataset to ensure they were used correctly. The methods are then applied to the set of histological images. In easy-to-understand images (cat images), the convolutional network is more consistent with human perception; on the contrary, in histological images of breast cancer, ViT-B-16 provided results much more similar to the expert's perception

EndoNet: model for automatic calculation of H-score on histological slides

H-score is a semi-quantitative method used to assess the presence and distribution of proteins in tissue samples by combining the intensity of staining and percentage of stained nuclei. It is widely used but time-consuming and can be limited in accuracy and precision. Computer-aided methods may help overcome these limitations and improve the efficiency of pathologists' workflows. In this work, we developed a model EndoNet for automatic calculation of H-score on histological slides. Our proposed method uses neural networks and consists of two main parts. The first is a detection model which predicts keypoints of centers of nuclei. The second is a H-score module which calculates the value of the H-score using mean pixel values of predicted keypoints. Our model was trained and validated on 1780 annotated tiles with a shape of 100x100 $\mu m$ and performed 0.77 mAP on a test dataset. Moreover, the model can be adjusted to a specific specialist or whole laboratory to reproduce the manner of calculating the H-score. Thus, EndoNet is effective and robust in the analysis of histology slides, which can improve and significantly accelerate the work of pathologists

EndoNuke: Nuclei Detection Dataset for Estrogen and Progesterone Stained IHC Endometrium Scans

We present EndoNuke, an open dataset consisting of tiles from endometrium immunohistochemistry slides with the nuclei annotated as keypoints. Several experts with various experience have annotated the dataset. Apart from gathering the data and creating the annotation, we have performed an agreement study and analyzed the distribution of nuclei staining intensity

Deep Semantic Segmentation of Angiogenesis Images

Angiogenesis is the development of new blood vessels from pre-existing ones. It is a complex multifaceted process that is essential for the adequate functioning of human organisms. The investigation of angiogenesis is conducted using various methods. One of the most popular and most serviceable of these methods in vitro is the short-term culture of endothelial cells on Matrigel. However, a significant disadvantage of this method is the manual analysis of a large number of microphotographs. In this regard, it is necessary to develop a technique for automating the annotation of images of capillary-like structures. Despite the increasing use of deep learning in biomedical image analysis, as far as we know, there still has not been a study on the application of this method to angiogenesis images. To the best of our knowledge, this article demonstrates the first tool based on a convolutional Unet++ encoder–decoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. The first annotated dataset in this field, AngioCells, is also being made publicly available. To create this dataset, participants were recruited into a markup group, an annotation protocol was developed, and an interparticipant agreement study was carried out