提出门控渐进融合网络，提升肠镜下息肉重识别精度
GPF-Net: Gated Progressive Fusion Learning for Polyp Re-Identification
- 用门控机制逐层融合多尺度特征，增强细节表达
- 在标准数据集上优于现有单模态模型，小目标识别效果显著
- 适合医学图像中细小病变的跨视角匹配任务
结肠镜息肉重识别旨在从多视角、多相机拍摄的大规模图库中匹配同一息肉，对结直肠癌的辅助诊断至关重要。然而，由于高阶特征分辨率粗略，难以捕捉小目标的关键细节，导致性能受限。为此，本文提出门控渐进融合网络（GPF-Net），通过全连接式门控机制，有选择地融合多层级特征。进一步设计门控渐进融合策略，实现多层次特征交互下的语义信息逐层优化。在标准基准测试中，该方法在多模态设置下显著优于当前最优的单模态重识别模型，尤其在小目标场景下表现突出。
原文摘要 · Abstract (English)
Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras, which plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis. However, the coarse resolution of high-level features of a specific polyp often leads to inferior results for small objects where detailed information is important. To address this challenge, we propose a novel architecture, named Gated Progressive Fusion network, to selectively fuse features from multiple levels using gates in a fully connected way for polyp ReID. On the basis of it, a gated progressive fusion strategy is introduced to achieve layer-wise refinement of semantic information through multi-level feature interactions. Experiments on standard benchmarks show the benefits of the multimodal setting over state-of-the-art unimodal ReID models, especially when combined with the specialized multimodal fusion strategy.
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