Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images
This work addresses the challenge of limited annotated data in medical imaging for precision medicine, though it appears incremental as it builds on existing multi-task learning approaches.
The authors tackled the problem of predicting patient response to treatment and survival in oncology using 3D PET images by proposing a multi-task learning framework, which achieved an area under the ROC curve of 77% for treatment response and 71% for survival, outperforming single-task methods.
Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To that end, radiomics was proposed as a field of study where images are used instead of invasive methods. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and can be physician subjective. Automated tools based on supervised deep learning have made great progress to assist physicians. However, they are data hungry, and annotated data remains a major issue in the medical field where only a small subset of annotated images is available. Methods: In this work, we propose a multi-task learning framework to predict patient's survival and response. We show that the encoder can leverage multiple tasks to extract meaningful and powerful features that improve radiomics performance. We show also that subsidiary tasks serve as an inductive bias so that the model can better generalize. Results: Our model was tested and validated for treatment response and survival in lung and esophageal cancers, with an area under the ROC curve of 77% and 71% respectively, outperforming single task learning methods. Conclusions: We show that, by using a multi-task learning approach, we can boost the performance of radiomic analysis by extracting rich information of intratumoral and peritumoral regions.