Abstract
Objective: This study aims to forecast the progression of lung cancer by estimating the future diameter of lung nodules. Methods: This approach uses as input the tabular data, axial images from tomography scans, and both data types, employing a ResNet50 model for image feature extraction and direct analysis of patient information for tabular data. The data are processed through a neural network before prediction. In the training phase, class weights are assigned based on the rarity of different types of nodules within the dataset, in alignment with nodule management guidelines. Results: Tabular data alone yielded the most accurate results, with a mean absolute deviation of 0.99 mm. For malignant nodules, the best performance, marked by a deviation of 2.82 mm, was achieved using tabular data applying Lung-RADS class weights during training. The tabular data results highlight the influence of using the initial nodule size as an input feature. These results surpass the literature reference of 348-day volume doubling time for malignant nodules. Conclusion: The developed predictive model is optimized for integration into a clinical workflow after detecting, segmenting, and classifying nodules. It provides accurate growth forecasts, establishing a more objective basis for determining follow-up intervals. Significance: With lung cancer's low survival rates, the capacity for precise nodule growth prediction represents a significant breakthrough. This methodology promises to revolutionize patient care and management, enhancing the chances for early risk assessment and effective intervention.

Abstract
Lung cancer represents a significant health concern necessitating diligent monitoring of individuals at risk. While the detection of pulmonary nodules warrants clinical attention, not all cases require immediate surgical intervention, often calling for a strategic approach to follow-up decisions. The Lung-RADS guideline serves as a cornerstone in clinical practice, furnishing structured recommendations based on various nodule characteristics, including size, calcification, and texture, outlined within established reference tables. However, the reliance on labor-intensive manual measurements underscores the potential advantages of integrating decision support systems into this process. Herein, we propose a feature-based methodology aimed at enhancing clinical decision-making by automating the assessment of nodules in computed tomography scans. Leveraging algorithms tailored for nodule calcification, texture analysis, and segmentation, our approach facilitates the automated classification of follow-up recommendations aligned with Lung-RADS criteria. Comparison with a previously reported end-to-end image-based classification method revealed competitive performance, with the feature-based approach achieving an accuracy of 0.701 +/- 0.026, while the end-to-end method attained 0.727 +/- 0.020. The inherent explainability of the feature-based approach offers distinct advantages, allowing clinicians to scrutinize and modify individual features to address disagreements or rectify inaccuracies, thereby tailoring follow-up recommendations to patient profiles.

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
Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resourceconstrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge. © 2023 Reyna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract
This paper is concerned with the semantic segmentation within domain-specific contexts, such as those pertaining to biology, physics, or material science. Under these circumstances, the objects of interest are often irregular and have fine structure, i.e., detail at arbitrarily small scales. Empirically, they are often understood as self-similar processes, a concept grounded in Multifractal Analysis. We find that this multifractal behaviour is carried out through a convolutional neural network (CNN), if we view its channel-wise responses as self-similar measures. A function of the local singularities of each measure we call Singularity Stregth Recalibration (SSR) is set forth to modulate the response at each layer of the CNN. SSR is a lightweight, plug-in module for CNNs. We observe that it improves a baseline U-Net in two biomedical tasks: skin lesion and colonic polyp segmentation, by an average of 1.36% and 1.12% Dice score, respectively. To the best of our knowledge, this is the first time multifractal-analysis is conducted end-to-end for semantic segmentation.