2026 Volume 44 Issue 4 Pages 174-179
In medical image analysis, deep learning has enabled highly accurate recognition; however, several challenges remain, including the high cost of expert annotation, inter-institutional domain gaps, class imbalance, appearance variations caused by anatomical location, and ambiguous boundaries between severity classes. This article introduces our studies that leverage intrinsic data structures in medical images, such as inter-image relationships, acquisition order, anatomical location information, cluster structures, and severity ordering, as weak supervisory signals or learning constraints. Specifically, we review soft- and self-constrained clustering for group-based labeling, label-efficient representation learning using anatomical location and acquisition order, and semi-supervised domain adaptation based on cluster and ordinal structures. Through these studies, we discuss how robust computer-aided diagnosis models can be constructed from limited supervisory information by incorporating human-interpretable data structures into the learning process.
