Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology.

Ghaffari Laleh, Narmin; Muti, Hannah Sophie; Loeffler, Chiara Maria Lavinia; Echle, Amelie; Saldanha, Oliver Lester; Mahmood, Faisal; Lu, Ming Y; Trautwein, Christian; Langer, Rupert; Dislich, Bastian; Buelow, Roman D; Grabsch, Heike Irmgard; Brenner, Hermann; Chang-Claude, Jenny; Alwers, Elizabeth; Brinker, Titus J; Khader, Firas; Truhn, Daniel; Gaisa, Nadine T; Boor, Peter; ... (2022). Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Medical image analysis, 79, p. 102474. Elsevier 10.1016/j.media.2022.102474

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Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised: the ground truth is only known for the slide, not for every single tile. In classical weakly-supervised analysis pipelines, all tiles inherit the slide label while in multiple-instance learning (MIL), only bags of tiles inherit the label. However, it is still unclear how these widely used but markedly different approaches perform relative to each other. We implemented and systematically compared six methods in six clinically relevant end-to-end prediction tasks using data from N=2980 patients for training with rigorous external validation. We tested three classical weakly-supervised approaches with convolutional neural networks and vision transformers (ViT) and three MIL-based approaches with and without an additional attention module. Our results empirically demonstrate that histological tumor subtyping of renal cell carcinoma is an easy task in which all approaches achieve an area under the receiver operating curve (AUROC) of above 0.9. In contrast, we report significant performance differences for clinically relevant tasks of mutation prediction in colorectal, gastric, and bladder cancer. In these mutation prediction tasks, classical weakly-supervised workflows outperformed MIL-based weakly-supervised methods for mutation prediction, which is surprising given their simplicity. This shows that new end-to-end image analysis pipelines in computational pathology should be compared to classical weakly-supervised methods. Also, these findings motivate the development of new methods which combine the elegant assumptions of MIL with the empirically observed higher performance of classical weakly-supervised approaches. We make all source codes publicly available at https://github.com/KatherLab/HIA, allowing easy application of all methods to any similar task.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Service Sector > Institute of Pathology

UniBE Contributor:

Dislich, Bastian

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

1361-8423

Publisher:

Elsevier

Language:

English

Submitter:

Pubmed Import

Date Deposited:

20 May 2022 14:51

Last Modified:

02 Jun 2022 00:16

Publisher DOI:

10.1016/j.media.2022.102474

PubMed ID:

35588568

Uncontrolled Keywords:

Artificial intelligence Computational pathology Convolutional neural networks Multiple-Instance Learning Vision transformers Weakly-supervised deep learning

BORIS DOI:

10.48350/170129

URI:

https://boris.unibe.ch/id/eprint/170129

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