用正态分布约束注意力，提升3D医学图像切片定位精度
Normal Guidance is what Attention Needs
- 用正态分布引导注意力分布，避免模型依赖中心偏置
- 在超400万张切片上，切片定位准确率显著优于现有方法
- 适合需要精准定位病变的医学影像分析任务
本文研究仅使用整幅3D医学图像二值标签进行训练时，能否实现切片级别的精确分类。在弱监督设置下，基于注意力的多实例学习（MIL）可为每一切片生成注意力分数。然而，近期研究表明，一种忽略图像内容、仅聚焦中心区域的简单基线方法，在3D脑部扫描的切片分类中已超越注意力和Transformer-based MIL方法。本文证明该基线在胸部与腹部CT扫描的切片分类中同样表现更优。为此，我们提出正态引导（Normal Guidance）正则化技术，促使学习到的注意力分布呈现钟形曲线。在三个医学影像数据集（总计超过400万张2D切片）上，本方法使基于注意力和Transformer的MIL模型在切片级定位性能上显著优于当前最优水平，同时在整图分类任务中保持竞争力。
原文摘要 · Abstract (English)
We consider training classifiers for 3D medical images using only one binary label for the entire volume rather than a label for each 2D slice. In such weakly supervised settings, can we learn accurate classifiers for slice-level predictions? Attention-based multiple instance learning (MIL) can produce an attention score for every slice. Yet recent work demonstrates that a simple center-focused baseline that ignores image content can outperform attention-based and transformer-based MIL at slice-level classification of 3D brain scans. We show this baseline also outperforms existing MIL at slice-level classification of thoracic and abdominal CT scans. Motivated by this baseline, we propose Normal Guidance, a regularization technique that encourages the learned attention distribution to follow a bell-shaped curve. Across three medical imaging datasets totaling over 4 million 2D slices, we show our Normal Guidance enables attention-based and transformer-based MIL methods to deliver significantly better slice-level localization than the state-of-the-art while remaining competitive at whole-scan classification.
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