学习聚焦：用于医学图像分类的解剖学引导注意力正则化

中文摘要 AI 辅助

医学图像分类模型理想情况下应在预测时识别诊断相关区域，但标准分类损失很少提供空间监督。通过解剖形状信息（如任务相关解剖结构的分割掩码）进行显式监督可引导网络关注目标预测相关区域，但获取此类掩码需大量人工标注和计算开销。随着分割基础模型的出现，我们利用其在多种成像模态中对解剖结构的强定位能力，无需训练专用分割模型即可提取解剖形状先验。本文提出新框架Locus，即解剖学注意力正则化框架，利用预训练分割基础模型引导分类器关注多种成像模态中具有诊断意义的解剖结构。我们引入正则化项自适应平衡解剖（前景）和背景区域的注意力，背景注意力占主导时对分类器进行惩罚。我们在八个不同医学成像数据集上验证了Locus，显示出分类性能持续提升以及解剖学基础注意力得到改善。

英文摘要

Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classification losses rarely provide spatial supervision. Explicit supervision via anatomical shape information, such as segmentation masks of task-relevant anatomy, has been shown to guide the network toward regions relevant to the target prediction. However, obtaining such masks incurs substantial manual annotation effort and computational overhead. With the advent of segmentation foundation models that exhibit strong localization of anatomical structures across diverse imaging modalities, we leverage this capability to extract anatomical shape priors without the burden of training a dedicated segmentation model. In this paper, we propose a new framework, Locus, an anatomical attention regularization framework that leverages pretrained segmentation foundation models to guide a classifier's attention toward diagnostically meaningful anatomical structures across diverse imaging modalities. Instead of enforcing pixel-wise alignment with the foundation-model-derived mask, we introduce a regularization term that adaptively balances attention between anatomical (foreground) and background regions, penalizing the classifier when background attention dominates. We validate Locus on eight diverse medical imaging datasets spanning dermoscopy, X-ray, histopathology, and cardiac MRI, showing consistent gains in classification performance alongside improved anatomically grounded attention.

发表机构

• University of Virginia（弗吉尼亚大学）
• Ahsanullah University of Science and Technology（阿山努拉科技大学）
• University of Utah（犹他大学）

机构由 AI 辅助整理，请以论文原文为准。