Abstract #3516

Multi-Task Learning: Segmentation as an auxiliary task for Survival Prediction of cancer using Deep Learning

José Maria da Silva Moreira^1,2, João da Silva Santinha^1,2, Thomas Varsavsky³, Carole Sudre³, Jorge Cardoso³, Mário Figueiredo², and Nickolas Papanikolaou¹

¹Computational Clinical Imaging Group, Champalimaud Center for the Unknown, Lisbon, Portugal, ²Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal, ³Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

This work presents a new method for multi-task learning that aims to increase the performance of the regression task, using the support of the segmentation task. While requiring further validation to guarantee the increase in performance, the preliminary data of this study suggests that using a ”helper” function might increase performance on the main task. In our study, a better performance of the survival prediction model was observed on the validation set when using the multi-task network, compared to a simpler single-task process.

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