MSA-DCNN: A Data-Efficient Multi-Scale Deformable CNN for Medical Image Classification
For medical image analysis practitioners, this provides a data-efficient model that handles multi-scale structures and label scarcity, though improvements are incremental over existing methods.
MSA-DCNN introduces a scale-consistent deformable attention framework for medical image classification that jointly handles multi-scale morphology and limited annotations. It achieves competitive or better performance than ViT, CNN, and semi-supervised baselines on three public benchmarks and an external leukaemia dataset, with fewer parameters.
Existing deep learning methods perform well in medical image classification but struggle with multi-scale morphology and limited annotations due to fixed sampling and data-hungry training. Existing approaches address these challenges in isolation: DCN-based models provide adaptive sampling but lack explicit multi-scale attention fusion and label-efficient regularisation; multi-scale architectures typically rely on static fusion; and semi-supervised methods target label scarcity without jointly modelling adaptive cross-scale representations. We propose MSA-DCNN, a scale-consistent deformable attention learning framework that introduces adaptive multi-scale sampling, within-scale saliency refinement, learned cross-scale fusion, and auxiliary self-distillation within a unified optimisation scheme, with potential to generalise to structurally heterogeneous anatomy. We evaluate on three public benchmarks and an external hold-out set for leukaemia. MSA-DCNN demonstrates competitive and often better performance against ViT baselines, CNN baselines, and a MICCAI semi-supervised baseline under distribution shift and label scarcity in accuracy, F1, and AUC (binary), while using fewer parameters. Ablations confirm complementary component contributions, supporting MSA-DCNN as a practical foundation for data-efficient medical image classification.