Multiple Instance Learning (MIL) is the predominant framework for classifying gigapixel whole-slide images in computational pathology. MIL follows a sequence of 1) extracting patch features, 2) applying a linear layer to obtain task-specific patch features, and 3) aggregating the patches into a slide feature for classification. While substantial efforts have been devoted to optimizing patch feature extraction and aggregation, none have yet addressed the second point, the critical layer which transforms general-purpose features into task-specific features. We hypothesize that this layer constitutes an overlooked performance bottleneck and that stronger representations can be achieved with a low-rank transformation tailored to each patch's phenotype, yielding synergistic effects with any of the existing MIL approaches. To this end, we introduce MAMMOTH, a parameter-efficient, multi-head mixture of experts module designed to improve the performance of any MIL model with minimal alterations to the total number of parameters. Across eight MIL methods and 19 different classification tasks, we find that such task-specific transformation has a larger effect on performance than the choice of aggregation method. For instance, when equipped with MAMMOTH, even simple methods such as max or mean pooling attain higher average performance than any method with the standard linear layer. Overall, MAMMOTH improves performance in 130 of the 152 examined configurations, with an average $+3.8\%$ change in performance. Code is available at https://github.com/mahmoodlab/mammoth.

翻译：多实例学习（MIL）是计算病理学中对千兆像素全切片图像进行分类的主要框架。MIL遵循以下步骤：1）提取图像块特征；2）应用线性层获取任务特定图像块特征；3）将图像块聚合为切片特征以进行分类。尽管已有大量研究致力于优化图像块特征提取与聚合，但尚未有工作关注第二个关键步骤——将通用特征转化为任务特定特征的线性层。我们假设该层构成了一个被忽视的性能瓶颈，且通过为每个图像块的表型定制低秩变换，可以实现更强的特征表示，从而与现有MIL方法产生协同效应。为此，我们提出MAMMOTH——一种参数高效的多头专家混合模块，旨在以最小参数改动提升任意MIL模型的性能。在八种MIL方法与19种不同分类任务的实验中，我们发现这种任务特定变换对性能的影响大于聚合方法的选择。例如，配备MAMMOTH后，即使采用最大池化或平均池化等简单方法，其平均性能也优于采用标准线性层的任意方法。总体而言，MAMMOTH在152种实验配置中提升了130种配置的性能，平均性能提升+3.8%。代码已开源：https://github.com/mahmoodlab/mammoth。