Our crossMoDA challenge to be held MICCAI 2023 is now live!
CAI4CAI members and alumni are leading the organization of the new edition of the cross-modality Domain Adaptation challenge (crossMoDA) for medical image segmentation Challenge, which will runs as an official challenge during the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2023 conference.
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. By encouraging algorithms to be robust to unseen situations or different input data domains, Domain Adaptation improves the applicability of machine learning approaches to various clinical settings. While a large variety of DA techniques has been proposed, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly address single-class problems. To tackle these limitations, the crossMoDA challenge introduced the first large and multi-class dataset for unsupervised cross-modality Domain Adaptation.
Compared to the previous crossMoDA instance, which made use of multi-institutional data acquired in controlled conditions for radiosurgery planning and focused on a 2 class segmentation task (tumour and cochlea), the 2023 edition extends the segmentation task by including multi-institutional, heterogenous data acquired for routine surveillance purposes and introduces a sub-segmentation for the tumour (intra- and extra-meatal components) thereby leading to a 3 class problem.
More information about the challenge here.
Navodini Wijethilake
PhD Student
Navodini is a MRC-DTP student supervised by Mr. Jonathan Shapey and Prof. Tom Vercauteren.
Reuben Dorent
Alumni
Reuben is a PhD student supervised by Prof. Tom Vercauteren and Prof. Sebastien Ourselin. Reuben’s research focuses on collaborative learning of joint tasks from various medical centres with different local resources.
Marina Ivory
Research Associate
Marina is a Research Associate with clinical experience in general radiology.
Aaron Kujawa
Research Associate
Aaron is a Research Associate working on the automatic segmentation of Vestibular Schwannoma.
Samuel Joutard
Alumni
Samuel is a PhD student supervised by Dr. Marc Modat and Prof. Tom Vercauteren. His research focuses on learning based registration with a special focus on tasks involving long range complex deformations.
Jonathan Shapey
Clinical Academic and Consultant Neurosurgeon
Jonathan’s academic interest focuses on the application of medical technology and artificial intelligence to neurosurgery.
Tom Vercauteren
Professor of Interventional Image Computing
Tom’s research interests include machine learning and computer assisted interventions