Weak labels for deep-learning-based detection of brain aneurysms from MR angiography scans

Tommaso Di Noto, Guillaume Marie, Sebastien Tourbier, Yasser Alemán-Gómez, Oscar Esteban, Guillaume Saliou, Meritxell Bach Cuadra, Patric Hagmann, Jonas Richiardi

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Unruptured Intracranial Aneurysms (UIAs) are focal dilatations in cerebral arteries. If overlooked, UIAs can rupture and lead to subarachnoid hemorrhages. Deep Learning (DL) models currently reach state-of-the-art performances for the automated detection of UIAs in Magnetic Resonance Angiography. However, there are still a few missing pieces to create robust DL models that can generalize across sites and be used during clinical practice. On one hand, the need for voxel-wise annotations from medical experts is hindering the creation of large datasets. On the other hand, multi-site validations are unfeasible since there exists to date only one open-access dataset. In this work, we summarize a full paper that we recently submitted to a journal and whose main contributions are the following: (a) a DL training approach that leverages fast-to-create weak labels and (b) the release of a second open-access dataset (the largest in the community) to foster model generalization.
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Friday 8th July
Poster Session 3.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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