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Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentation.

, , , , , , and . BrainLes@MICCAI, volume 10154 of Lecture Notes in Computer Science, page 119-128. (2016)

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Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks., , , , , , , , , and 3 other author(s). CoRR, (2019)CT Brain Perfusion: A Clinical Perspective., and . BrainLes@MICCAI (1), volume 11383 of Lecture Notes in Computer Science, page 15-24. Springer, (2018)Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction., , , , , , and . BrainLes@MICCAI (2), volume 11384 of Lecture Notes in Computer Science, page 429-440. Springer, (2018)The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification., , , , , , , , , and 35 other author(s). CoRR, (2021)The Federated Tumor Segmentation (FeTS) Challenge., , , , , , , , , and 22 other author(s). CoRR, (2021)Combining unsupervised and supervised learning for predicting the final stroke lesion., , , , , , and . CoRR, (2021)Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge., , , , , , , , , and 41 other author(s). CoRR, (2018)Federated Learning on Heterogeneous Diffusion-Weighted Imaging Data for Acute Stroke Infarct Segmentation., , , , , , , , , and 1 other author(s). ISBI, page 1-5. IEEE, (2023)CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias., , , and . BrainLes@MICCAI, volume 10154 of Lecture Notes in Computer Science, page 100-107. (2016)Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging., , , , , , , , , and 32 other author(s). MLCN@MICCAI, volume 13596 of Lecture Notes in Computer Science, page 115-124. Springer, (2022)