An aggregation of aggregation methods in computational pathology

Bilal, Mohsin; Jewsbury, Robert; Wang, Ruoyu; AlGhamdi, Hammam M.; Asif, Amina; Eastwood, Mark; Rajpoot, Nasir M.

doi:10.1016/j.media.2023.102885

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…In particular, note that the comparison in section 5.3 highlights the optimal performance across those five MIL algorithms. For a comprehensive review of aggregation methods in weakly-supervised learning, including MIL, see the recent work of (63). During the training process, we retain a random subset of 1,000 tile features for each slide.…”

Section: Models: Weakly-supervised Learningmentioning

confidence: 99%

Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling

Filiot¹,

Ghermi²,

Olivier³

et al. 2023

Preprint

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Computational pathology is revolutionizing the field of pathology by integrating advanced computer vision and machine learning technologies into diagnostic workflows. Recently, self-supervised learning (SSL) has emerged as a promising solution to learn representations from histology patches, leveraging large volumes of unannotated whole slide images (WSI). In particular, Masked Image Modeling (MIM) showed remarkable results and robustness over purely contrastive learning methods. In this work, we explore the application of MIM to histology using iBOT, a self-supervised transformer-based framework. Through a wide range of downstream tasks over seven cancer indications, we provide recommendations on the pre-training of large models for histology data using MIM. First, we demonstrate that in-domain pre-training with iBOT outperforms both ImageNet pre-training and a model pre-trained with a purely contrastive learning objective, MoCo V2. Second, we show that Vision Transformers models (ViT), when scaled appropriately, have the capability to learn pan-cancer representations that benefit a large variety of downstream tasks. Finally, our iBOT ViT-Base model, pre-trained on more than 40 million histology images from 16 different cancer types, achieves state-of-the-art performance in most weakly-supervised WSI classification tasks compared to other SSL frameworks.

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Section: Models: Weakly-supervised Learningmentioning

confidence: 99%

Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling

Filiot¹,

Ghermi²,

Olivier³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Examples of such predictions include patient survival, treatment response, genetic expression/mutation, and the origin of primary tumor [ 35 , 36 , 37 ]. Regardless of the level of application, results of the analysis may need to be aggregated to generate higher‐level predictions [ 38 ].…”

Section: The Promise Of Ai In Computational Pathologymentioning

confidence: 99%

Unleashing the potential of AI for pathology: challenges and recommendations

Asif

Rajpoot

Graham

et al. 2023

The Journal of Pathology

Self Cite

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Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer‐reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre‐clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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“…MIL frameworks rely on specific strategies for tile selection, feature extraction, and multiple inference aggregation. Common MIL paradigms rely on one training "bag" per image after careful tile selection (e.g., tissue content above a threshold) (12)(13)(14) ; data augmentations (12)(13)(14)(15)(16)(17) ; use of transfer learning from ImageNet via GoogLeNet, InceptionNet, ResNet, and MobileNet (18,19) for cancer, and AlexNet and ResNet for NAFLD (20,21) ; aggregation of tile-level inferences via max-pooling, or of tile-level features via average-pooling, attention-based or RNN based frameworks ahead of WSI-level prediction (12,13,16,21,22) .…”

Section: Deep Learning In Histopathologymentioning

confidence: 99%

Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning

Naik¹,

Forlano²,

Manousou³

et al. 2023

Biol. Imaging

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Non-alcoholic fatty liver disease (NAFLD) is now the leading cause of chronic liver disease, affecting approximately 30% of people worldwide. Histopathology reading of fibrosis patterns is crucial to diagnosing NAFLD. In particular, separating mild from severe stages corresponds to a critical transition as it correlates with clinical outcomes. Deep Learning for digitized histopathology whole-slide images (WSIs) can reduce high inter- and intra-rater variability. We demonstrate a novel solution to score fibrosis severity on a retrospective cohort of 152 Sirius-Red WSIs, with fibrosis stage annotated at slide level by an expert pathologist. We exploit multiple instance learning and multiple-inferences to address the sparsity of pathological signs. We achieved an accuracy of $ 78.98\pm 5.86\% $ , an F1 score of $ 77.99\pm 5.64\%, $ and an AUC of $ 0.87\pm 0.06 $ . These results set new state-of-the-art benchmarks for this application.

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An aggregation of aggregation methods in computational pathology

Cited by 10 publications

References 44 publications

Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling

Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling

Unleashing the potential of AI for pathology: challenges and recommendations

Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning

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