BEL: A Bag Embedding Loss for Transformer enhances Multiple Instance Whole Slide Image Classification
This is an incremental improvement for AI-based tumor classification in histopathology, addressing a domain-specific bottleneck in multiple instance learning.
The paper tackled the problem of weak supervisory signals in high-magnification whole slide image classification by proposing a Bag Embedding Loss (BEL) for transformers, resulting in improved performance over baselines on BRACS and CAMELYON17 datasets.
Multiple Instance Learning (MIL) has become the predominant approach for classification tasks on gigapixel histopathology whole slide images (WSIs). Within the MIL framework, single WSIs (bags) are decomposed into patches (instances), with only WSI-level annotation available. Recent MIL approaches produce highly informative bag level representations by utilizing the transformer architecture's ability to model the dependencies between instances. However, when applied to high magnification datasets, problems emerge due to the large number of instances and the weak supervisory learning signal. To address this problem, we propose to additionally train transformers with a novel Bag Embedding Loss (BEL). BEL forces the model to learn a discriminative bag-level representation by minimizing the distance between bag embeddings of the same class and maximizing the distance between different classes. We evaluate BEL with the Transformer architecture TransMIL on two publicly available histopathology datasets, BRACS and CAMELYON17. We show that with BEL, TransMIL outperforms the baseline models on both datasets, thus contributing to the clinically highly relevant AI-based tumor classification of histological patient material.