Adversarial learning of cancer tissue representations

Autor: Roderick Murray-Smith, Aristotelis Tsirigos, Anna H. Yeaton, Wisuwat Sunhem, Adalberto Claudio Quiros, Ke Yuan, Nicolas Coudray
Jazyk: angličtina
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
Tissue architecture
Computer Science - Machine Learning
Computer Vision and Pattern Recognition (cs.CV)
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Computer Science - Computer Vision and Pattern Recognition
Machine Learning (cs.LG)
03 medical and health sciences
Adversarial system
0302 clinical medicine
FOS: Electrical engineering
electronic engineering
information engineering
medicine
030304 developmental biology
Cognitive science
0303 health sciences
business.industry
Deep learning
Image and Video Processing (eess.IV)
fungi
Cancer
Electrical Engineering and Systems Science - Image and Video Processing
medicine.disease
ComputingMethodologies_PATTERNRECOGNITION
Tumor progression
Ask price
030220 oncology & carcinogenesis
Artificial intelligence
Psychology
business
Zdroj: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872366
MICCAI (8)
Popis: 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
Accepted for publication at MICCAI 2021
Databáze: OpenAIRE
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