提升医学影像多实例学习的定位精度，通过建模邻近实例标签一致性。
Sm: enhanced localization in Multiple Instance Learning for medical imaging classification
- 基于邻近实例标签相似性，设计新型局部依赖建模机制。
- 在定位任务上达到当前最优性能，分类性能也保持领先。
- 可灵活集成至各类全局依赖模型，适用于医疗影像分析场景。
多实例学习（MIL）广泛应用于医学影像分类以减少标注成本。训练仅需包级别标签，但通常需同时完成包级分类和实例级定位任务。早期方法将包内实例独立处理，近期方法虽考虑了实例间的全局与局部依赖，但在定位性能上仍有限。我们指出这些模型主要针对分类任务设计，对实例级信息挖掘不足。基于‘邻近实例更可能具有相同标签’的观察，提出一种新颖、合理且灵活的局部依赖建模机制，可独立使用或与任意全局依赖机制（如Transformer）结合。大量实证验证表明，该模块在定位任务上实现当前最优表现，同时在分类任务中亦具竞争力或优势。代码已开源：https://github.com/Franblueee/SmMIL。
原文摘要 · Abstract (English)
Multiple Instance Learning (MIL) is widely used in medical imaging classification to reduce the labeling effort. While only bag labels are available for training, one typically seeks predictions at both bag and instance levels (classification and localization tasks, respectively). Early MIL methods treated the instances in a bag independently. Recent methods account for global and local dependencies among instances. Although they have yielded excellent results in classification, their performance in terms of localization is comparatively limited. We argue that these models have been designed to target the classification task, while implications at the instance level have not been deeply investigated. Motivated by a simple observation -- that neighboring instances are likely to have the same label -- we propose a novel, principled, and flexible mechanism to model local dependencies. It can be used alone or combined with any mechanism to model global dependencies (e.g., transformers). A thorough empirical validation shows that our module leads to state-of-the-art performance in localization while being competitive or superior in classification. Our code is at https://github.com/Franblueee/SmMIL.
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