Deep learning with visual explanation for radiotherapy-induced toxicity prediction

Behnaz Elhaminia; Alexandra Gilbert; Alejandro F. Frangi; Andrew Scarsbrook; John Lilley; Ane Appelt; Ali Gooya

doi:10.1117/12.2652481

7 April 2023 Deep learning with visual explanation for radiotherapy-induced toxicity prediction

Behnaz Elhaminia, Alexandra Gilbert, Alejandro F. Frangi, Andrew Scarsbrook, John Lilley, Ane Appelt, Ali Gooya

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124651V (2023) https://doi.org/10.1117/12.2652481
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Deep learning models are widely studied for radiotherapy toxicity prediction; however, one of the major challenges is that they are complex models and difficult to understand. To aid in the creation of optimal dose treatment plans, it is critical to understand the mechanism and reasoning behind the network’s prediction, as well as the specific anatomical regions involved in toxicity. In this work, we propose a convolutional neural network to predict the toxicity after pelvic radiotherapy that is able to explain the network’s prediction. The proposed model analyses the dose treatment plan using multiple instance learning and convolutional encores. A dataset of 315 patients was included in the study, and experiments with both quantitative and qualitative approaches were conducted to assess the network’s performance.

Citation Download Citation

Behnaz Elhaminia, Alexandra Gilbert, Alejandro F. Frangi, Andrew Scarsbrook, John Lilley, Ane Appelt, and Ali Gooya "Deep learning with visual explanation for radiotherapy-induced toxicity prediction", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124651V (7 April 2023); https://doi.org/10.1117/12.2652481

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