- nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation nnunet
- AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem? AbdomenCT-1K
- Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models CTPelvic1K
- CTSpine1k: A large-scale dataset for spinal vertebrae segmentation in computed tomography CTSpine1k
- CoTr: Efficient 3D Medical Image Segmentation by bridging CNN and Transformer CoTr
- UNet++: A Nested U-Net Architecture for Medical Image Segmentation UNet++
- nnFormer: Interleaved Transformer for Volumetric Segmentation nnFormer
- Efficient Context-Aware Network for Abdominal Multi-organ Segmentation EfficientSegmentation
- 3D Self-Supervised Methods for Medical Imaging 3D Self-Supervised
- DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets DoDNet
- State-of-the-art medical image segmentation methods based on various challenges! SOTA-MedSeg
- Semi-supervised-learning-for-medical-image-segmentation SSL4MIS
- Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation UA-MT
- Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images SASSnet
- 3D Medical Image Segmentation With Distance Transform Maps SegWithDistMap
- Examinee-Examiner Network: Weakly Supervised Accurate Coronary Lumen Segmentation using Centerline Constraint Examinee-Examiner-Network
- Kiu-net: Overcomplete convolutional architectures for biomedical image and volumetric segmentationKiu-net
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