bibtext: @InProceedings{ Nov_Ataskconditional_MICCAI2024,
                   author = { Novosad, Philip and Carano, Richard A. D. and Krishnan, Anitha Priya },
                   title = { { A task-conditional mixture-of-experts model for missing modality segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: Accurate quantification of multiple sclerosis (MS) lesions using multi-contrast magnetic resonance imaging (MRI) plays a crucial role in disease assessment. While many methods for automatic MS lesion segmentation in MRI are available, these methods typically require a fixed set of MRI modalities as inputs. Such full multi-contrast inputs are not always acquired, limiting their utility in practice. To address this issue, a training strategy known as modality dropout (MD) has been widely adopted in the literature. However, models trained via MD still under-perform compared to dedicated models trained for particular modality configurations. In this work, we hypothesize that the poor performance of MD is the result of an overly constrained multi-task optimization problem. To reduce harmful task interference, we propose to incorporate task-conditional mixture-of-expert layers into our segmentation model, allowing different tasks to leverage different parameters subsets. Second, we propose a novel online self-distillation loss to help regularize the model and to explicitly promote model invariance to input modality configuration. Compared to standard MD training, our method demonstrates improved results on a large proprietary clinical trial dataset as well as on a small publicly available dataset of T2 lesions.
title: A task-conditional mixture-of-experts model for missing modality segmentation
authors: Novosad, Philip Carano, Richard A. D. Krishnan, Anitha Priya