Realistic Data Enrichment for Robust Image Segmentation in Histopathology
Cechnicka S, Ball J, Reynaud H, Arthurs C, Roufosse C, Kainz B (2024)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2024
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 14293 LNCS
Pages Range: 63-72
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Vancouver, BC, CAN
ISBN: 9783031458569
DOI: 10.1007/978-3-031-45857-6_7
Abstract
Poor performance of quantitative analysis in histopathological Whole Slide Images (WSI) has been a significant obstacle in clinical practice. Annotating large-scale WSIs manually is a demanding and time-consuming task, unlikely to yield the expected results when used for fully supervised learning systems. Rarely observed disease patterns and large differences in object scales are difficult to model through conventional patient intake. Prior methods either fall back to direct disease classification, which only requires learning a few factors per image, or report on average image segmentation performance, which is highly biased towards majority observations. Geometric image augmentation is commonly used to improve robustness for average case predictions and to enrich limited datasets. So far no method provided sampling of a realistic posterior distribution to improve stability, e.g. for the segmentation of imbalanced objects within images. Therefore, we propose a new approach, based on diffusion models, which can enrich an imbalanced dataset with plausible examples from underrepresented groups by conditioning on segmentation maps. Our method can simply expand limited clinical datasets making them suitable to train machine learning pipelines, and provides an interpretable and human-controllable way of generating histopathology images that are indistinguishable from real ones to human experts. We validate our findings on two datasets, one from the public domain and one from a Kidney Transplant study. 1 (The source code and trained models will be publicly available at the time of the conference, on huggingface and github. )
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United Kingdom (GB)How to cite
APA:
Cechnicka, S., Ball, J., Reynaud, H., Arthurs, C., Roufosse, C., & Kainz, B. (2024). Realistic Data Enrichment for Robust Image Segmentation in Histopathology. In Lisa Koch, M. Jorge Cardoso, Enzo Ferrante, Konstantinos Kamnitsas, Mobarakol Islam, Meirui Jiang, Nicola Rieke, Sotirios A. Tsaftaris, Dong Yang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 63-72). Vancouver, BC, CAN: Springer Science and Business Media Deutschland GmbH.
MLA:
Cechnicka, Sarah, et al. "Realistic Data Enrichment for Robust Image Segmentation in Histopathology." Proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, Vancouver, BC, CAN Ed. Lisa Koch, M. Jorge Cardoso, Enzo Ferrante, Konstantinos Kamnitsas, Mobarakol Islam, Meirui Jiang, Nicola Rieke, Sotirios A. Tsaftaris, Dong Yang, Springer Science and Business Media Deutschland GmbH, 2024. 63-72.
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