FPN-IAIA-BL：一种用于数字乳腺X线摄影中肿块边缘分类的多尺度可解释深度学习模型
FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography
AI总结：
本文提出多尺度可解释深度学习模型FPN-IAIA-BL，通过用户可配置的粗细粒度原型解决现有可解释模型特征定位不准的问题，实现与放射科医生实践一致的乳腺X线摄影肿块边缘分类。
摘要：
数字乳腺X线摄影对乳腺癌检测至关重要，深度学习为更快、更准确的乳腺X线图像分析提供了有前景的工具。在放射学和其他高风险环境中，不可解释的（“黑箱”）深度学习模型并不适用，这些领域呼吁构建可解释模型。近期可解释计算机视觉研究通过利用原型进行基于案例的解释，为这些此前的黑箱提供了透明度，并在包括乳腺X线摄影在内的应用中取得了高准确率。然而，这些模型在精确特征定位方面存在困难，当图像中只有一小部分相关时，却对图像的大部分区域进行推理。本文针对这一不足，提出了一种新颖的多尺度可解释深度学习模型，用于乳腺X线摄影肿块边缘分类。我们的贡献不仅提供了一个推理方式与放射科医生实践一致的可解释模型，还提供了一种通用的计算机视觉架构，支持用户配置从粗粒度到细粒度的原型。
英文摘要：
Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse- to fine-grained prototypes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
