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

Xiaoqing Zhang, Zunjie Xiao, Xiao Wu, Yanlin Chen, Jilu Zhao, Yan Hu, Jiang Liu

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)

Abstract

Spatial attention (SA) 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, the 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 multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling (CCPP) to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization (PN), and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCA network (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 (CL). The extensive experiments on six medical image datasets show that the PPCANet outperforms state-of-the-art (SOTA) attention-based networks and recent DNNs. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusAccepted/In press - 2024
Externally publishedYes

Keywords

  • Aggregates
  • Biomedical imaging
  • Computer science
  • Cross-channel pyramid pooling (CCPP)
  • Decision making
  • Image classification
  • Lesions
  • Task analysis
  • explanation
  • medical image analysis
  • pyramid pixel context adaption
  • supervised contrastive learning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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