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.

翻译：近年来，自动腕部骨折识别的研究获得了广泛关注。在实际医疗场景中，医师可能缺乏准确解读X光片所需的专业知识，这凸显了利用机器视觉提升诊断准确性的必要性。然而，传统识别技术在分类腕部病理时面临挑战，难以辨别X光片中的细微差异，因为许多病理（如骨折）可能非常微小且难以区分。本研究将腕部病理识别视为一个细粒度视觉识别（FGVR）问题，利用一个有限的、自定义收集的数据集，该数据集模拟了现实世界的医疗限制，仅依赖图像级标注。我们提出了一种基于FGVR的专用集成方法，以识别X光片中的判别性区域。我们采用了一种名为Grad-CAM的可解释人工智能（XAI）技术来精确定位这些区域。我们的集成方法在性能上超越了许多传统的SOTA和FGVR技术，这证明了我们的策略在提升腕部病理识别准确性方面的有效性。