Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification
This work improves multi-instance learning for medical image analysis, specifically in pathology, by incorporating semi-supervision to reduce biases, though it is incremental as it builds on existing MIL methods.
The paper tackles the problem of whole-slide image classification by addressing inter-slide variability and leveraging free annotations from normal patches, resulting in significant performance improvements on public datasets like Camelyon-16 and TCGA lung cancer.
Whole-slide image (WSI) classification is a challenging task because 1) patches from WSI lack annotation, and 2) WSI possesses unnecessary variability, e.g., stain protocol. Recently, Multiple-Instance Learning (MIL) has made significant progress, allowing for classification based on slide-level, rather than patch-level, annotations. However, existing MIL methods ignore that all patches from normal slides are normal. Using this free annotation, we introduce a semi-supervision signal to de-bias the inter-slide variability and to capture the common factors of variation within normal patches. Because our method is orthogonal to the MIL algorithm, we evaluate our method on top of the recently proposed MIL algorithms and also compare the performance with other semi-supervised approaches. We evaluate our method on two public WSI datasets including Camelyon-16 and TCGA lung cancer and demonstrate that our approach significantly improves the predictive performance of existing MIL algorithms and outperforms other semi-supervised algorithms. We release our code at https://github.com/AITRICS/pathology_mil.