Abstract
In this work, we assess the impact of self-supervised learning (SSL) approaches on the detection of gastritis atrophy (GA) and intestinal metaplasia (IM) conditions. GA and IM are precancerous gastric lesions. Detecting these lesions is crucial to intervene early and prevent their progression to cancer. A set of experiments is conducted over the Chengdu dataset, by considering different amounts of annotated data in the training phase. Our results reveal that, when all available data is used for training, SSL approaches achieve a classification accuracy on par with a supervised learning baseline, (81.52% vs 81.76%). Interestingly, we observe that in low-data regimes (here represented as retaining only 12.5% of annotated data for training), the SSL model guarantees an accuracy gain with respect to the supervised learning baseline of approximately 1.5% (73.00% vs 71.52%). This observation hints at the potential of SSL models in leveraging unlabeled data, thus showcasing more robust performance improvements and generalization. Experimental results also show that SSL performance is significantly dependent on the specific data augmentation techniques and parameters adopted for contrastive learning, thus advocating for further investigations into the definition of optimal data augmentation frameworks specifically tailored for gastric lesion detection applications.

Abstract
Early diagnosis of cardiovascular diseases is essential for an effective treatment, potentially preventing severe health complications and improving clinical outcomes. Electrocardiogram (ECG) and phonocardiogram (PCG) are costeffective, noninvasive diagnostic tools providing crucial and complementary information about the heart's electrical and mechanical activities. This paper presents a novel approach to the assessment of cardiovascular health through the multimodal analysis of simultaneously recorded ECG and PCG signals. Combining multimodal analysis and transfer learning on publicly available data, the most successful multimodal approach achieved an accuracy of 82.79%, a ROC AUC score of 91.26%, and a recall of 93.10% demonstrating the potential of these techniques. This study provides a foundation for future research aimed at enhancing the performance of multimodal cardiac abnormality detection systems.

Abstract
The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, which is a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia, and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FMs), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FMs in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FMs into clinical practice for the prevention/management of GC cases, thereby improving patient outcomes.

Abstract
The medical imaging field contends with limited data for training deep learning (DL) models. Our study evaluated traditional data augmentation (DA) and Generative Adversarial Networks (GANs) in enhancing DL models for identifying stomach precancerous lesions. Classic DA consistently outperformed GAN-based methods with ResNet50 (0.94 vs 0.93 accuracy) and ViT (0.85 vs 0.84 accuracy) models achieving higher accuracy and other performance metrics with DA compared to GANs. Despite this, GAN augmentation showed significant improvements when compared to train with the original dataset, highlighting its role in diversifying datasets and aiding generalization across different medical imaging datasets. Combining both augmentation techniques can enhance model robustness and generalisation capabilities in DL applications for medical diagnostics, leveraging DA's consistency and GANs' diversity. © 2025 Elsevier B.V.. All rights reserved.