Electrocardiogram (ECG) delineation plays a crucial role in assisting cardiologists with accurate diagnoses. Prior research studies have explored various methods, including the application of deep learning techniques, to achieve precise delineation. However, existing approaches face limitations primarily related to dataset size and robustness.In this paper, we introduce a dataset for ECG delineation and propose a novel self-trained method aimed at leveraging a vast amount of unlabeled ECG data. Our approach involves the pseudolabeling of unlabeled data using a neural network trained on our dataset. Subsequently, we train the model on the newly labeled samples to enhance the quality of delineation. We conduct experiments demonstrating that our dataset is a valuable resource for training robust models and that our proposed self-trained method improves the prediction quality of ECG delineation.

As invisible image watermarking gains importance for verifying AI-generated content, consistency and reproducibility remain major challenges due to the diverse methods, datasets, attacks, and metrics.We aim to provide a flexible, extensible, and user-friendly framework that enables systematic testing of watermarking methods under various conditions.We developed WIBE, a framework with command-line interfaces and YAML configuration support, enabling users to evaluate a wide range of image watermarking algorithms on various datasets, apply configurable attack scenarios, and compute standard performance metrics. WIBE includes a library of pre-implemented methods and supports integration of new watermarking techniques, attacks, metrics, and datasets through a plugin-based architecture.WIBE enables rapid prototyping, reproducible experiments, and insightful comparison of watermarking robustness. In our demo, we present its core features, plugin extensibility, and interactive infographics, making it a practical tool for researchers and practitioners working at the intersection of AI and media integrity.Project on GitHub: https://github.com/ispras/wibeYouTube video: https://youtu.be/lbWWB1crrwk

In this study, we introduce a novel method, called$\text{MamT}^4$, 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.

The increasing adoption of smartwatches has concurrently expanded the potential for ongoing health monitoring via single-lead ECG recordings. This advancement has led to new possibilities for automatically identifying heart conditions. Neural networks have become one of the most popular methods for handling the classification of ECGs, achieving notable successes in this field. However, there are significant challenges that need to be addressed. One such challenge is the lack of labeled single-lead ECG data. Another one is that single-lead ECGs contain less diagnostic information compared to traditional 12-lead ECGs, which limits the effectiveness of neural networks in ECG classification. Addressing these challenges, in this paper we introduce a novel approach that leverages the multi-view structure of ECG records in a contrastive learning paradigm. Our method not only involves creating positive pairs through augmentations of the original signal but also through diverse combinations of leads from the same ECG. After pretraining models on 12-lead ECGs, we fine-tune them using single-lead ECGs to improve classification performance. We compare the proposed method with existing contrastive learning frameworks and show its superior performance in the classification of cardiac abnormalities.