Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology

arXiv:2411.09373 · eess.IV, cs.CV, cs.LG · Submitted 2024-11-14 · Read on arXiv

eess.IV, cs.CV, cs.LG

Submitted: 2024-11-14

Updated: 2024-11-14

Comments: Poster at NeurIPS 2024

Journal ref: Advances in Neural Information Processing Systems 37 (2024) 43499-43532

DOI: 10.52202/079017-1378

Code: https://github.com/undercutspiky/SFL

License: http://creativecommons.org/publicdomain/zero/1.0/

The gist: Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue

Terminology

Abstract

Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue and imaging equipment. We hypothesise that focusing on nuclei can improve the out-of-domain (OOD) generalisation in cancer detection. We propose a simple approach to improve OOD generalisation for cancer detection by focusing on nuclear morphology and organisation, as these are domain-invariant features critical in cancer detection. Our approach integrates original images with nuclear segmentation masks during training, encouraging the model to prioritise nuclei and their spatial arrangement. Going beyond mere data augmentation, we introduce a regularisation technique that aligns the representations of masks and original images. We show, using multiple datasets, that our method improves OOD generalisation and also leads to increased robustness to image corruptions and adversarial attacks. The source code is available at https://github.com/undercutspiky/SFL/

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