Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning
This work addresses limitations in medical image classification by improving attention mechanisms for subtle lesion detection, though it appears incremental as it builds on existing attention and contrastive learning approaches.
The paper tackled the problem of poor performance of spatial attention mechanisms in medical image analysis by proposing a Pyramid Pixel Context Adaption (PPCA) module and supervised contrastive learning, resulting in PPCANet outperforming state-of-the-art methods on six medical image datasets.
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.