MSA-DCNN: A Data-Efficient Multi-Scale Deformable CNN for Medical Image Classification

• URL: http://arxiv.org/abs/2607.06083v1
• Date: Tue, 07 Jul 2026 09:52:46 GMT
• Title: MSA-DCNN: A Data-Efficient Multi-Scale Deformable CNN for Medical Image Classification
• Abstract summary: Existing deep learning methods struggle with multi-scale morphology and limited annotations due to fixed sampling and data-hungry training.<n>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.
• Score: 1.6566053195631467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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