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Labeling medical images depends on professional knowledge, making itdifficult to acquire large amount of annotated medical images with high qualityin a short time. Thus, making good use of limited labeled samples in a smalldataset to build a high-performance model is the key to medical imageclassification problem. In this paper, we propose a deeply supervised LayerSelective Attention Network (LSANet), which comprehensively uses labelinformation in feature-level and prediction-level supervision. Forfeature-level supervision, in order to better fuse the low-level features andhigh-level features, we propose a novel visual attention module, LayerSelective Attention (LSA), to focus on the feature selection of differentlayers. LSA introduces a weight allocation scheme which can dynamically adjustthe weighting factor of each auxiliary branch during the whole training processto further enhance deeply supervised learning and ensure its generalization.For prediction-level supervision, we adopt the knowledge synergy strategy topromote hierarchical information interactions among all supervision branchesvia pairwise knowledge matching. Using the public dataset, MedMNIST, which is alarge-scale benchmark for biomedical image classification covering diversemedical specialties, we evaluate LSANet on multiple mainstream CNNarchitectures and various visual attention modules. The experimental resultsshow the substantial improvements of our proposed method over its correspondingcounterparts, demonstrating that LSANet can provide a promising solution forlabel-efficient learning in the field of medical image classification.

Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification