Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology
Journal:
arXiv
Published Date:
Jan 6, 2025
Abstract
Multiple Instance Learning (MIL) methods allow for gigapixel Whole-Slide
Image (WSI) analysis with only slide-level annotations. Interpretability is
crucial for safely deploying such algorithms in high-stakes medical domains.
Traditional MIL methods offer explanations by highlighting salient regions.
However, such spatial heatmaps provide limited insights for end users. To
address this, we propose a novel inherently interpretable WSI-classification
approach that uses human-understandable pathology concepts to generate
explanations. Our proposed Concept MIL model leverages recent advances in
vision-language models to directly predict pathology concepts based on image
features. The model's predictions are obtained through a linear combination of
the concepts identified on the top-K patches of a WSI, enabling inherent
explanations by tracing each concept's influence on the prediction. In contrast
to traditional concept-based interpretable models, our approach eliminates the
need for costly human annotations by leveraging the vision-language model. We
validate our method on two widely used pathology datasets: Camelyon16 and
PANDA. On both datasets, Concept MIL achieves AUC and accuracy scores over 0.9,
putting it on par with state-of-the-art models. We further find that 87.1\%
(Camelyon16) and 85.3\% (PANDA) of the top 20 patches fall within the tumor
region. A user study shows that the concepts identified by our model align with
the concepts used by pathologists, making it a promising strategy for
human-interpretable WSI classification.
