Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

TL;DR

This paper introduces PPCANet, a lightweight neural network with a novel pyramid pixel context adaption module, enhanced by supervised contrastive learning, to improve medical image classification by better capturing subtle lesion regions.

Contribution

The paper proposes the PPCA module for dynamic pixel recalibration and integrates supervised contrastive learning, advancing medical image classification performance.

Findings

PPCANet outperforms existing attention-based networks on six datasets.

The PPCA module effectively captures multi-scale pixel context.

Supervised contrastive learning improves feature representation.

Abstract

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.