Spatial Decomposition For Robust Domain Adaptation In Prostate Cancer Detection

The utility of high-quality imaging of Prostate Cancer (PCa) using 3.0 Tesla MRI (versus 1.5 Tesla) is well established, yet a vast majority of MRI units across many countries are 1.5 Tesla. Recently, Deep Learning has been applied successfully to augment radiological interpretation of medical image...

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Bibliographic Details
Published in:	2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) pp. 1218 - 1222
Main Authors:	Grebenisan, Andrew, Sedghi, Alireza, Izard, Jason, Siemens, Robert, Menard, Alexandre, Mousavi, Parvin
Format:	Conference Proceeding
Language:	English
Published:	IEEE 13-04-2021
Subjects:	Adaptation models Data models Deep learning Domain Standardization Magnetic resonance imaging Prostate Cancer Standardization Training Transfer learning
Online Access:	Get full text
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Summary:	The utility of high-quality imaging of Prostate Cancer (PCa) using 3.0 Tesla MRI (versus 1.5 Tesla) is well established, yet a vast majority of MRI units across many countries are 1.5 Tesla. Recently, Deep Learning has been applied successfully to augment radiological interpretation of medical images. However, training such models requires very large amount of data, and often the models do not generalize well to data with different acquisition parameters. To address this, we introduce domain standardization, a novel method that enables image synthesis between domains by separating anatomy- and modality-related factors of images. Our results show an improved PCa classification with an AUC of 0.75 compared to traditional transfer learning methods. We envision domain standardization to be applied as a promising tool towards enhancing the interpretation of lower resolution MRI images, reducing the barriers of the potential uptake of deep models for jurisdictions with smaller populations.
ISSN:	1945-8452
DOI:	10.1109/ISBI48211.2021.9433779