Beyond classification: Whole slide tissue histopathology analysis by end-to-end part learning Conference Paper

  


Authors: Xie, C.; Muhammad, H.; Vanderbilt, C. M.; Caso, R.; Yarlagadda, D. V. K.; Campanella, G.; Fuchs, T. J.
Title: Beyond classification: Whole slide tissue histopathology analysis by end-to-end part learning
Conference Title: Medical Imaging with Deep Learning (MIDL 2020)
Abstract: An emerging technology in cancer care and research is the use of histopathology whole slide images (WSI). Leveraging computation methods to aid in WSI assessment poses unique challenges. WSIs, being extremely high resolution giga-pixel images, cannot be directly processed by convolutional neural networks (CNN) due to huge computational cost. For this reason, state-of-the-art methods for WSI analysis adopt a two-stage approach where the training of a tile encoder is decoupled from the tile aggregation. This results in a trade-off between learning diverse and discriminative features. In contrast, we propose end-to-end part learning (EPL) which is able to learn diverse features while ensuring that learned features are discriminative. Each WSI is modeled as consisting of k groups of tiles with similar features, defined as parts. A loss with respect to the slide label is backpropagated through an integrated CNN model to k input tiles that are used to represent each part. Our experiments show that EPL is capable of clinical grade prediction of prostate and basal cell carcinoma. Further, we show that diverse discriminative features produced by EPL succeeds in multi-label classification of lung cancer architectural subtypes. Beyond classification, our method provides rich information of slides for high quality clinical decision support.
Journal Title Proceedings of Machine Learning Research
Volume: 121
Conference Dates: 2022 Jul 6-8
Conference Location: Montreal, Canada
ISBN: 2640-3498
Publisher: Journal Machine Learning Research  
Date Published: 2020-01-01
Start Page: 843
End Page: 856
Language: English
ACCESSION: WOS:001231286000058
PROVIDER: wos
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in the PDF -- Corresponding authors is MSK author: PDF provides emails for all MSK authors -- Proceedings Paper -- 3rd Conference on Medical Imaging with Deep Learning (MIDL) -- JUL 06-08, 2020 -- ELECTR NETWORK -- Source: Wos