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Proceedings Paper

Multi-task learning for mortality prediction in LDCT images
Author(s): Hengtao Guo; Melanie Kruger; Ge Wang; Mannudeep K. Kalra; Pingkun Yan
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Paper Abstract

Low-dose CT (LDCT) has been commonly used for lung cancer screening and it is much desirable to computerize the image analysis for risk evaluation to reduce healthcare disparities. While informative structural image features can be extracted from medical images using state-of-the-art deep neural networks, other quantitative clinical measurements can also contribute to the overall assessment but are often ignored by researchers and also difficult to obtain. This work introduces a multi-task learning framework, which can simultaneously extract image features from LDCT images and estimate the clinical measurements for all-cause mortality risk prediction. The proposed method is a hybrid neural network with multi-scale input and multi-task supervision labels. The presented work shows that the extracted feature vectors have improved mortality prediction as they are generated to include both abstracted image features and high-level clinical knowledge.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142C (16 March 2020); doi: 10.1117/12.2549387
Show Author Affiliations
Hengtao Guo, Rensselaer Polytechnic Institute (United States)
Melanie Kruger, Shenendehowa High School (United States)
Ge Wang, Rensselaer Polytechnic Institute (United States)
Mannudeep K. Kalra, Massachusetts General Hospital (United States)
Pingkun Yan, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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