We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on hold-out estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.<\/p>\n","protected":false},"excerpt":{"rendered":"
We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on hold-out estimates, (2) increased compactness at a tree-level, thereby yielding shallow […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Springer, Cham","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"171","msr_page_range_end":"183","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"MICCAI Brain Lesion 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