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The proposed method prevents over-fitting by learning to discriminate\nbetween true and fake patches obtained by a generator network. Our work extends\ncurrent adversarial learning approaches, which focus on 2D single-modality\nimages, to the more challenging context of 3D volumes of multiple modalities.\nThe proposed method is evaluated on the problem of segmenting brain MRI from\nthe iSEG-2017 and MRBrainS 2013 datasets. Significant performance improvement\nis reported, compared to state-of-art segmentation networks trained in a\nfully-supervised manner. In addition, our work presents a comprehensive\nanalysis of different GAN architectures for semi-supervised segmentation,\nshowing recent techniques like feature matching to yield a higher performance\nthan conventional adversarial training approaches. Our code is publicly\navailable at https://github.com/arnab39/FewShot_GAN-Unet3D","url_abs":"http://arxiv.org/abs/1810.12241v1","url_pdf":"http://arxiv.org/pdf/1810.12241v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"few-shot-3d-multi-modal-medical-image","repo_url":"https://github.com/arnab39/FewShot_GAN-Unet3D","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-medical-imaging-segmentation","task_name":"3D Medical Imaging Segmentation"},{"task_slug":"brain-image-segmentation","task_name":"Brain Image Segmentation"},{"task_slug":"brain-segmentation","task_name":"Brain Segmentation"},{"task_slug":"few-shot-image-segmentation","task_name":"Few-Shot Semantic Segmentation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"semi-supervised-semantic-segmentation","task_name":"Semi-Supervised Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"feature-matching","method_name":"GAN Feature Matching"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.12241","atlas_url":"https://app.syntology.ai/?focus=1810.12241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.12241"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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