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Brain Tumor Segmentation Based on 3D Unet with Multi-Class Focal Loss
Research article (2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018) · cited 33× · AI/ML
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APA4ort.xyz Knowledge Graph. (2026). Brain Tumor Segmentation Based on 3D Unet with Multi-Class Focal Loss. Retrieved May 24, 2026, from https://4ort.xyz/entity/brain-tumor-segmentation-based-on-3d-unet-with-multi-class-focal-loss
MLA“Brain Tumor Segmentation Based on 3D Unet with Multi-Class Focal Loss.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/brain-tumor-segmentation-based-on-3d-unet-with-multi-class-focal-loss.
BibTeX@misc{4ortxyz_brain-tumor-segmentation-based-on-3d-unet-with-multi-class-focal-loss_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Brain Tumor Segmentation Based on 3D Unet with Multi-Class Focal Loss}}, year = {2026}, url = {https://4ort.xyz/entity/brain-tumor-segmentation-based-on-3d-unet-with-multi-class-focal-loss}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Brain Tumor Segmentation Based on 3D Unet with Multi-Class Focal Loss — https://4ort.xyz/entity/brain-tumor-segmentation-based-on-3d-unet-with-multi-class-focal-loss (retrieved 2026-05-24)
