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|Title:||Self-supervised lesion change detection and localisation in longitudinal multiple sclerosis brain imaging|
|Citation:||Lecture Notes in Artificial Intelligence, 2021 / DeBruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (ed./s), vol.12907 LNCS, pp.670-680|
|Publisher:||Springer International Publishing|
|Series/Report no.:||Lecture Notes in Computer Science|
|Conference Name:||24th International Conference on Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (27 Sep 2021 - 1 Oct 2021 : Strasbourg)|
|Minh-Son, G. Sarno, Chee Chong, Mark Jenkinson, Gustavo Carneiro|
|Abstract:||Longitudinal imaging forms an essential component in the management and follow-up of many medical conditions. The presence of lesion changes on serial imaging can have significant impact on clinical decision making, highlighting the important role for automated change detection. Lesion changes can represent anomalies in serial imaging, which implies a limited availability of annotations and a wide variety of possible changes that need to be considered. Hence, we introduce a new unsupervised anomaly detection and localisation method trained exclusively with serial images that do not contain any lesion changes. Our training automatically synthesises lesion changes in serial images, introducing detection and localisation pseudo-labels that are used to self-supervise the training of our model. Given the rarity of these lesion changes in the synthesised images, we train the model with the imbalance robust focal Tversky loss. When compared to supervised models trained on different datasets, our method shows competitive performance in the detection and localisation of new demyelinating lesions on longitudinal magnetic resonance imaging in multiple sclerosis patients. Code for the models will be made available at https://github.com/toson87/MSChangeDetection.|
|Keywords:||Change detection; Siamese networks; multiple sclerosis|
|Rights:||© Springer Nature Switzerland AG 2021|
|Appears in Collections:||Computer Science publications|
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