The field of medical imaging analysis is moving towards more sophisticated and robust methods for image classification, abnormality detection, and report generation. Researchers are exploring new architectures and techniques, such as graph-based frameworks and transformer-based models, to improve the accuracy and generalization of their models. Longitudinal data is also being increasingly incorporated into these models to capture the temporal context of medical images. Furthermore, there is a growing emphasis on explainability and attention alignment in deep learning models to mitigate biases and improve their reliability. Noteworthy papers in this area include:

• A paper proposing a graph-based framework for multi-label abnormality classification in 3D Chest CT scans, which achieves strong cross-dataset generalization and competitive performance compared to state-of-the-art visual encoders.
• A paper introducing a curated benchmark dataset for laryngeal cancer staging, which provides a reproducible foundation for AI-driven research in this area.