Multi-task learning for mortality prediction in LDCT images

While informative structural image features can be extracted from low-dose CT (LDCT) 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.


H. Guo, M. Kruger, G. Wang, M.K. Kalra, P. Yan, " Multi-task learning for mortality prediction in LDCT images ,"

Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142C, Houston, Texas, 15-22 February 2020.