做医学影像分析的团队终于有了一个简单有效的正则化技巧——Normal Guidance能显著提升弱监督下的切片定位精度，比现有MIL方法更准，建议做3D医学图像分类的开发者试试。
在3D医学图像分析中，通常只有整个体积的单一标签，而非每张2D切片都有标签。弱监督学习下，注意力机制的多实例学习（MIL）可为每张切片生成注意力分数，但近期研究发现，一个忽略图像内容的简单中心聚焦基线方法，在脑部扫描切片分类上竟优于基于注意力和Transformer的MIL方法。该研究进一步验证，该基线在胸部和腹部CT扫描的切片分类上也表现更优。为此，作者提出Normal Guidance正则化技术，鼓励学习到的注意力分布遵循钟形曲线。在三个医学影像数据集（总计超过400万张2D切片）上，Normal Guidance使基于注意力和Transformer的MIL方法在切片级定位上显著优于现有技术，同时在全扫描分类上保持竞争力。
Normal Guidance is what Attention Needs
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.