  Labeling medical images depends on professional knowledge, making it
difficult to acquire large amount of annotated medical images with high quality
in a short time. Thus, making good use of limited labeled samples in a small
dataset to build a high-performance model is the key to medical image
classification problem. In this paper, we propose a deeply supervised Layer
Selective Attention Network (LSANet), which comprehensively uses label
information in feature-level and prediction-level supervision. For
feature-level supervision, in order to better fuse the low-level features and
high-level features, we propose a novel visual attention module, Layer
Selective Attention (LSA), to focus on the feature selection of different
layers. LSA introduces a weight allocation scheme which can dynamically adjust
the weighting factor of each auxiliary branch during the whole training process
to further enhance deeply supervised learning and ensure its generalization.
For prediction-level supervision, we adopt the knowledge synergy strategy to
promote hierarchical information interactions among all supervision branches
via pairwise knowledge matching. Using the public dataset, MedMNIST, which is a
large-scale benchmark for biomedical image classification covering diverse
medical specialties, we evaluate LSANet on multiple mainstream CNN
architectures and various visual attention modules. The experimental results
show the substantial improvements of our proposed method over its corresponding
counterparts, demonstrating that LSANet can provide a promising solution for
label-efficient learning in the field of medical image classification.
