用细粒度集成方法提升有限X光片中腕部病变识别精度
Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset
- 基于细粒度视觉识别，通过集成模型定位病灶区域
- 在自建有限数据集上超越多数主流方法
- 结合可解释AI技术，适合医疗影像辅助诊断场景
近年来，自动化腕部骨折识别受到广泛关注。临床实践中，医生常缺乏专业X光解读能力，亟需机器视觉提升诊断准确率。然而，传统识别方法难以区分细微差异，因许多病灶（如骨折）较小且难以分辨。本研究将腕部病理识别视为细粒度视觉识别（FGVR）问题，使用一个受限的、自建的数据集，仅依赖图像级标注。提出一种专用于FGVR的集成方法，识别X光片中的判别性区域，并采用可解释AI技术Grad-CAM定位这些区域。该集成方法在性能上优于多种主流SOTA及FGVR技术，验证了其在提升腕部病理识别准确率方面的有效性。
原文摘要 · Abstract (English)
The exploration of automated wrist fracture recognition has gained considerable research attention in recent years. In practical medical scenarios, physicians and surgeons may lack the specialized expertise required for accurate X-ray interpretation, highlighting the need for machine vision to enhance diagnostic accuracy. However, conventional recognition techniques face challenges in discerning subtle differences in X-rays when classifying wrist pathologies, as many of these pathologies, such as fractures, can be small and hard to distinguish. This study tackles wrist pathology recognition as a fine-grained visual recognition (FGVR) problem, utilizing a limited, custom-curated dataset that mirrors real-world medical constraints, relying solely on image-level annotations. We introduce a specialized FGVR-based ensemble approach to identify discriminative regions within X-rays. We employ an Explainable AI (XAI) technique called Grad-CAM to pinpoint these regions. Our ensemble approach outperformed many conventional SOTA and FGVR techniques, underscoring the effectiveness of our strategy in enhancing accuracy in wrist pathology recognition.
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