Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning
arxiv(2023)
摘要
Spatial attention mechanism has been widely incorporated into deep neural
networks (DNNs), significantly lifting the performance in computer vision tasks
via long-range dependency modeling. However, it may perform poorly in medical
image analysis. Unfortunately, existing efforts are often unaware that
long-range dependency modeling has limitations in highlighting subtle lesion
regions. To overcome this limitation, we propose a practical yet lightweight
architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which
exploits multi-scale pixel context information to recalibrate pixel position in
a pixel-independent manner dynamically. PPCA first applies a well-designed
cross-channel pyramid pooling to aggregate multi-scale pixel context
information, then eliminates the inconsistency among them by the well-designed
pixel normalization, and finally estimates per pixel attention weight via a
pixel context integration. By embedding PPCA into a DNN with negligible
overhead, the PPCANet is developed for medical image classification. In
addition, we introduce supervised contrastive learning to enhance feature
representation by exploiting the potential of label information via supervised
contrastive loss. The extensive experiments on six medical image datasets show
that PPCANet outperforms state-of-the-art attention-based networks and recent
deep neural networks. We also provide visual analysis and ablation study to
explain the behavior of PPCANet in the decision-making process.
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