CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and
Subtyping in Whole Slide Images
- URL: http://arxiv.org/abs/2305.05314v2
- Date: Tue, 10 Oct 2023 10:09:27 GMT
- Title: CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and
Subtyping in Whole Slide Images
- Authors: Olga Fourkioti, Matt De Vries and Chris Bakal
- Abstract summary: We propose the Context-Aware Multiple Instance Learning (CAMIL) architecture for cancer diagnosis.
CAMIL incorporates neighbor-constrained attention to consider dependencies among tiles within a Whole Slide Images (WSI) and integrates contextual constraints as prior knowledge into the MIL model.
We evaluate CAMIL on subtyping non-small cell lung cancer (TCGA-NSCLC) and detecting lymph node metastasis, achieving test AUCs of 0.959% and 0.975%, respectively.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The visual examination of tissue biopsy sections is fundamental for cancer
diagnosis, with pathologists analyzing sections at multiple magnifications to
discern tumor cells and their subtypes. However, existing attention-based
multiple instance learning (MIL) models, used for analyzing Whole Slide Images
(WSIs) in cancer diagnostics, often overlook the contextual information of
tumor and neighboring tiles, leading to misclassifications. To address this, we
propose the Context-Aware Multiple Instance Learning (CAMIL) architecture.
CAMIL incorporates neighbor-constrained attention to consider dependencies
among tiles within a WSI and integrates contextual constraints as prior
knowledge into the MIL model. We evaluated CAMIL on subtyping non-small cell
lung cancer (TCGA-NSCLC) and detecting lymph node (CAMELYON16) metastasis,
achieving test AUCs of 0.959\% and 0.975\%, respectively, outperforming other
state-of-the-art methods. Additionally, CAMIL enhances model interpretability
by identifying regions of high diagnostic value.
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