Claudio Quiros, A., Coudray, N., Yeaton, A., Sunhem, W., Murray-Smith, R. , Tsirigos, A. and Yuan, K. (2021) Adversarial Learning of Cancer Tissue Representations. In: 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), 27 Sept-1 Oct 2021, pp. 602-612. ISBN 9783030872366 (doi: 10.1007/978-3-030-87237-3_58)
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Abstract
Deep learning based analysis of histopathology images shows promise in advancing the understanding of tumor progression, tumor micro-environment, and their underpinning biological processes. So far, these approaches have focused on extracting information associated with annotations. In this work, we ask how much information can be learned from the tissue architecture itself. We present an adversarial learning model to extract feature representations of cancer tissue, without the need for manual annotations. We show that these representations are able to identify a variety of morphological characteristics across three cancer types: Breast, colon, and lung. This is supported by 1) the separation of morphologic characteristics in the latent space; 2) the ability to classify tissue type with logistic regression using latent representations, with an AUC of 0.97 and 85% accuracy, comparable to supervised deep models; 3) the ability to predict the presence of tumor in Whole Slide Images (WSIs) using multiple instance learning (MIL), achieving an AUC of 0.98 and 94% accuracy. Our results show that our model captures distinct phenotypic characteristics of real tissue samples, paving the way for further understanding of tumor progression and tumor micro-environment, and ultimately refining histopathological classification for diagnosis and treatment (The code and pretrained models are available at: https://github.com/AdalbertoCq/Adversarial-learning-of-cancer-tissue-representations).
| Item Type: | Conference Proceedings |
|---|---|
| Additional Information: | We will like to acknowledge funding support from University of Glasgow on A.C.Q scholarship, K.Y from EPSRC grant EP/R018634/1, and R.M-S. from EPSRC grants EP/T00097X/1 and EP/R018634/1. This work has used computing resources at the NYU School of Medicine High Performance Computing Facility. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Murray-Smith, Professor Roderick and Sunhem, Wisuwat and Claudio Quiros, Adalberto and Yuan, Dr Ke |
| Authors: | Claudio Quiros, A., Coudray, N., Yeaton, A., Sunhem, W., Murray-Smith, R., Tsirigos, A., and Yuan, K. |
| College/School: | College of Science and Engineering College of Science and Engineering > School of Computing Science |
| ISBN: | 9783030872366 |
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