ArXiv Preprint
In pre-clinical pathology, there is a paradox between the abundance of raw
data (whole slide images from many organs of many individual animals) and the
lack of pixel-level slide annotations done by pathologists. Due to time
constraints and requirements from regulatory authorities, diagnoses are instead
stored as slide labels. Weakly supervised training is designed to take
advantage of those data, and the trained models can be used by pathologists to
rank slides by their probability of containing a given lesion of interest. In
this work, we propose a novel contextualized eXplainable AI (XAI) framework and
its application to deep learning models trained on Whole Slide Images (WSIs) in
Digital Pathology. Specifically, we apply our methods to a
multi-instance-learning (MIL) model, which is trained solely on slide-level
labels, without the need for pixel-level annotations. We validate
quantitatively our methods by quantifying the agreements of our explanations'
heatmaps with pathologists' annotations, as well as with predictions from a
segmentation model trained on such annotations. We demonstrate the stability of
the explanations with respect to input shifts, and the fidelity with respect to
increased model performance. We quantitatively evaluate the correlation between
available pixel-wise annotations and explainability heatmaps. We show that the
explanations on important tiles of the whole slide correlate with tissue
changes between healthy regions and lesions, but do not exactly behave like a
human annotator. This result is coherent with the model training strategy.
Marco Bertolini, Van-Khoa Le, Jake Pencharz, Andreas Poehlmann, Djork-Arné Clevert, Santiago Villalba, Floriane Montanari
2023-02-03
