Feature Disentanglement to Aid Imaging Biomarker Characterization for Genetic Mutations

Padmaja Jonnalagedda, Brent Weinberg, Jason Allen, Bir Bhanu
Proceedings of the Third Conference on Medical Imaging with Deep Learning, PMLR 121:349-364, 2020.

Abstract

Various mutations have been shown to correlate with prognosis of High-Grade Glioma (Glioblastoma). Overall prognostic assessment requires analysis of multiple modalities: imaging, molecular and clinical. To optimize this assessment pipeline, this paper develops the first deep learning-based system that uses MRI data to predict 19/20 co-gain, a mutation that indicates median survival. It addresses two key challenges when dealing with deep learning algorithms and medical data: lack of data and high data imbalance. To tackle these challenges, we propose a unified approach that consists of a Feature Disentanglement based Generative Adversarial Network (FeaD-GAN) for generating synthetic images. FeaD-GAN projects disentangled features into a high dimensional space and re-samples them from a pseudo-large data distribution to generate synthetic images from very limited data. A thorough analysis is performed to (a) characterize aspects of visual manifestation of 19/20 co-gain to demonstrate the effectiveness of FeaD-GAN and (b) demonstrate that not only do the imaging biomarkers of 19/20 co-gain exist, but also that they are reproducible.

Cite this Paper


BibTeX
@InProceedings{pmlr-v121-jonnalagedda20a, title = {Feature Disentanglement to Aid Imaging Biomarker Characterization for Genetic Mutations}, author = {Jonnalagedda, Padmaja and Weinberg, Brent and Allen, Jason and Bhanu, Bir}, booktitle = {Proceedings of the Third Conference on Medical Imaging with Deep Learning}, pages = {349--364}, year = {2020}, editor = {Arbel, Tal and Ben Ayed, Ismail and de Bruijne, Marleen and Descoteaux, Maxime and Lombaert, Herve and Pal, Christopher}, volume = {121}, series = {Proceedings of Machine Learning Research}, month = {06--08 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v121/jonnalagedda20a/jonnalagedda20a.pdf}, url = { http://proceedings.mlr.press/v121/jonnalagedda20a.html }, abstract = {Various mutations have been shown to correlate with prognosis of High-Grade Glioma (Glioblastoma). Overall prognostic assessment requires analysis of multiple modalities: imaging, molecular and clinical. To optimize this assessment pipeline, this paper develops the first deep learning-based system that uses MRI data to predict 19/20 co-gain, a mutation that indicates median survival. It addresses two key challenges when dealing with deep learning algorithms and medical data: lack of data and high data imbalance. To tackle these challenges, we propose a unified approach that consists of a Feature Disentanglement based Generative Adversarial Network (FeaD-GAN) for generating synthetic images. FeaD-GAN projects disentangled features into a high dimensional space and re-samples them from a pseudo-large data distribution to generate synthetic images from very limited data. A thorough analysis is performed to (a) characterize aspects of visual manifestation of 19/20 co-gain to demonstrate the effectiveness of FeaD-GAN and (b) demonstrate that not only do the imaging biomarkers of 19/20 co-gain exist, but also that they are reproducible.} }
Endnote
%0 Conference Paper %T Feature Disentanglement to Aid Imaging Biomarker Characterization for Genetic Mutations %A Padmaja Jonnalagedda %A Brent Weinberg %A Jason Allen %A Bir Bhanu %B Proceedings of the Third Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2020 %E Tal Arbel %E Ismail Ben Ayed %E Marleen de Bruijne %E Maxime Descoteaux %E Herve Lombaert %E Christopher Pal %F pmlr-v121-jonnalagedda20a %I PMLR %P 349--364 %U http://proceedings.mlr.press/v121/jonnalagedda20a.html %V 121 %X Various mutations have been shown to correlate with prognosis of High-Grade Glioma (Glioblastoma). Overall prognostic assessment requires analysis of multiple modalities: imaging, molecular and clinical. To optimize this assessment pipeline, this paper develops the first deep learning-based system that uses MRI data to predict 19/20 co-gain, a mutation that indicates median survival. It addresses two key challenges when dealing with deep learning algorithms and medical data: lack of data and high data imbalance. To tackle these challenges, we propose a unified approach that consists of a Feature Disentanglement based Generative Adversarial Network (FeaD-GAN) for generating synthetic images. FeaD-GAN projects disentangled features into a high dimensional space and re-samples them from a pseudo-large data distribution to generate synthetic images from very limited data. A thorough analysis is performed to (a) characterize aspects of visual manifestation of 19/20 co-gain to demonstrate the effectiveness of FeaD-GAN and (b) demonstrate that not only do the imaging biomarkers of 19/20 co-gain exist, but also that they are reproducible.
APA
Jonnalagedda, P., Weinberg, B., Allen, J. & Bhanu, B.. (2020). Feature Disentanglement to Aid Imaging Biomarker Characterization for Genetic Mutations. Proceedings of the Third Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 121:349-364 Available from http://proceedings.mlr.press/v121/jonnalagedda20a.html .

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