Author: Laura Mora
Supervisors: Verónica Vilaplana
Presentation time: 11:15
Virtual Room: 3.4 | Live presentation URL
Automation of brain tumors in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is especially critical in medical diagnosis. This work studies 3D encoder-decoder architectures, trained with patching techniques to reduce memory consumption and decrease the effect of unbalanced data. We also introduce voxel-wise uncertainty information, both epistemic and aleatoric using test-time dropout and data-augmentation respectively.