GPF-Net：门控渐进融合学习用于息肉重识别
GPF-Net: Gated Progressive Fusion Learning for Polyp Re-Identification
AI总结：
GPF-Net通过门控渐进融合学习提升息肉重识别性能，结合多模态融合策略优于现有单模态模型。
摘要：
结肠镜息肉重识别旨在从大量画廊中匹配同一息肉的图像，这些图像来自不同视角的不同相机拍摄，这在计算机辅助诊断中对预防和治疗结直肠癌具有重要作用。然而，特定息肉的高阶特征的粗分辨率常常导致小物体的识别结果较差，尤其是当详细信息很重要时。为了解决这一挑战，我们提出了一种新的架构，称为门控渐进融合网络，通过全连接方式使用门控选择性融合多级特征进行息肉重识别。在此基础上，引入了门控渐进融合策略，通过多级特征交互实现语义信息的逐层细化。实验表明，多模态设置在标准基准上的优势优于最先进的单模态重识别模型，尤其是在结合专门的多模态融合策略时。
英文摘要：
Colonoscopic Polyp Re-Identification (ReID) aims to match the same polyp across a large gallery of images captured from different viewpoints and with different cameras, playing a critical role in computer-aided diagnosis for the prevention and treatment of colorectal cancer. However, the coarse granularity of high-level features often limits performance on small polyps, where fine-grained details are essential for accurate matching. To address this challenge, we propose a novel multimodal feature fusion architecture, termed the Gated Progressive Fusion Network, which selectively integrates features from multiple levels through fully connected gating mechanisms. Building on this framework, we introduce a gated progressive fusion strategy that enables layer-wise refinement of semantic information, facilitating multi-level feature interactions to enhance both generalization ability and robustness. Extensive experiments on standard benchmarks demonstrate the advantages of the multimodal setting over state-of-the-art unimodal ReID models, particularly when combined with the proposed fusion strategy tailored for general-purpose scenarios.
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