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.

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 the percentage of stained nuclei. It is widely used but time-consuming and can be limited in terms of 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 H-score calculation on histological slides. Our proposed method uses neural networks and consists of two main parts. The first is a detection model which predicts the keypoints of centers of nuclei. The second is an H-score module that 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 100 × 100 µm and we achieved 0.77 mAP on a test dataset. We obtained our best results in H-score calculation; these results proved superior to QuPath predictions. 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.

Background: Significant progress has been made in the field of machine learning, enabling the development of methods for automatic interpretation of medical images that provide high-quality diagnostics. However, most of these methods require access to confidential data, making them difficult to apply under strict privacy requirements. Existing privacy-preserving approaches, such as federated learning and dataset distillation, have limitations related to data access, visual interpretability, etc. Methods: This study explores the use of generative models to create synthetic medical data that preserves the statistical properties of the original data while ensuring privacy. The research is carried out on the VinDr-Mammo dataset of digital mammography images. A conditional generative method using Latent Diffusion Models (LDMs) is proposed with conditioning on diagnostic labels and lesion information. Diagnostic utility and privacy robustness are assessed via cancer classification tasks and re-identification tasks using Siamese neural networks and membership inference. Results: The generated synthetic data achieved a Fréchet Inception Distance (FID) of 5.8, preserving diagnostic features. A model trained solely on synthetic data achieved comparable performance to one trained on real data (ROC-AUC: 0.77 vs. 0.82). Visual assessments showed that synthetic images are indistinguishable from real ones. Privacy evaluations demonstrated a low re-identification risk (e.g., mAP@R = 0.0051 on the test set), confirming the effectiveness of the privacy-preserving approach. Conclusions: The study demonstrates that privacy-preserving generative models can produce synthetic medical images with sufficient quality for diagnostic task while significantly reducing the risk of patient re-identification. This approach enables secure data sharing and model training in privacy-sensitive domains such as medical imaging.

BACKGROUND: Breast cancer is one of the leading causes of cancer-related mortality in women [1]. Regular mass screening with mammography plays a critical role in the early detection of changes in breast tissue. However, the early stages of pathology often go undetected and are difficult to diagnose [2]. Despite the effectiveness of mammography in reducing breast cancer mortality, manual image analysis can be time consuming and labor intensive. Therefore, attempts to automate this process, for example using computer-aided diagnosis systems, are relevant [3]. In recent years, however, solutions based on neural networks have gained increasing interest, especially in biology and medicine [4-6]. Technological advances using artificial intelligence have already demonstrated their effectiveness in pathology detection [7, 8]. AIM: The study aimed to develop an automated solution to detect breast cancer on mammograms. MATERIALS AND METHODS: The solution is implemented as follows: a deep neural network-based tool has been developed to obtain the probability of malignancy from the input image. A combined dataset from public datasets such as MIAS, CBIS-DDSM, INbreast, CMMD, KAU-BCMD, and VinDr-Mammo [9–14] was used to train the model. RESULTS: The classification model, based on the EfficientNet-B3 architecture, achieved an area under the ROC curve of 0.95, a sensitivity of 0.88, and a specificity of 0.9 when tested on a sample from the combined dataset. The model’s high generalization ability, which is another advantage, was demonstrated by its ability to perform well on images from different datasets with varying data quality and acquisition regions. Furthermore, techniques such as image pre-cropping and augmentations during training were used to enhance the model's performance. CONCLUSIONS: The experimental results demonstrated that the model is capable of accurately detecting malignancies with a high degree of confidence. The obtained high-quality metrics offer a significant potential for implementing this method in automated diagnostics, for instance, as an additional opinion for medical specialists.

The article investigates the application of a neural network diagnosis model to histological images in order to detect plasma cells for chronic endometritis detection. A two-stage algorithm was developed for plasma cell detection. At the first stage, a CenterNet model was used to detect stromal and epithelial cells. The neural network was trained on an open dataset with histological images and further fine-tuned using an additional labeled dataset. A labeling protocol was used, and the coefficient of agreement between two experts was calculated, which turned out to be 0.81. At the second stage, using the developed algorithm based on computer vision methods, plasma cells were identified and their HSV color boundaries were calculated. For the two-stage algorithm the following quality metrics were obtained: precision = 0.70, recall = 0.43, f1-score = 0.53. The model then was modified to detect only plasma cells and trained on a dataset with histological images containing labeled plasma cells. The quality metrics of the modified detection model were obtained: precision = 0.73, recall = 0.89, f1-score = 0.8. As a result of the comparison, the modified detection model approach showed the best quality metrics. Automating the work of counting plasma cells will allow doctors to spend less time on routine activities.

In this study, we introduce a novel method, called MamT$^4$, which is used for simultaneous analysis of four mammography images. A decision is made based on one image of a breast, with attention also devoted to three additional images: another view of the same breast and two images of the other breast. This approach enables the algorithm to closely replicate the practice of a radiologist who reviews the entire set of mammograms for a patient. Furthermore, this paper emphasizes the preprocessing of images, specifically proposing a cropping model (U-Net based on ResNet-34) to help the method remove image artifacts and focus on the breast region. To the best of our knowledge, this study is the first to achieve a ROC-AUC of 84.0 $\pm$ 1.7 and an F1 score of 56.0 $\pm$ 1.3 on an independent test dataset of Vietnam digital mammography (VinDr-Mammo), which is preprocessed with the cropping model.