The field of medical imaging and prognosis is undergoing significant developments, driven by innovative applications of deep learning and machine learning techniques. A key direction of research is the improvement of multimodal prognosis models, which integrate multiple types of data to predict patient outcomes. Recent work has highlighted the challenges of generalizing these models across different cancer types and datasets, and has proposed novel solutions such as Dirac rebalancers and distribution entanglement to address these issues. Another area of focus is the development of uncertainty-aware multiple instance learning frameworks, which can provide more reliable and interpretable predictions in digital pathology applications. The use of ensembles of weak learners based on spectral total variation features has also shown promise in improving the accuracy of medical imaging tasks. Furthermore, confidence-based self-distillation approaches are being explored as a means of improving the performance and generalizability of deep learning models for tasks such as polyp segmentation. Notable papers in this area include:

• Single-Domain Generalization for Multimodal Cross-Cancer Prognosis via Dirac Rebalancer and Distribution Entanglement, which proposes a new task and modules to improve generalization across cancer types.
• SGPMIL: Sparse Gaussian Process Multiple Instance Learning, which introduces a probabilistic attention-based framework for multiple instance learning.
• The Power of Certainty: How Confident Models Lead to Better Segmentation, which presents a confidence-based self-distillation approach for improving polyp segmentation.