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Organ Segmentation

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Showing 101–125 of 305 papers

TitleStatusHype
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor SegmentationCode0
Abdominal multi-organ segmentation in CT using Swinunter—0
Gastro-Intestinal Tract Segmentation Using an Explainable 3D Unet—0
Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency—0
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image SegmentationCode1
A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ SegmentationCode2
Self-supervised Semantic Segmentation: Consistency over TransformationCode1
Abdominal Multi-Organ Segmentation Based on Feature Pyramid Network and Spatial Recurrent Neural Network—0
AutoProSAM: Automated Prompting SAM for 3D Multi-Organ SegmentationCode0
Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation—0
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation—0
Benchmarking Scalable Epistemic Uncertainty Quantification in Organ SegmentationCode0
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-trainingCode2
Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View Fusion—0
SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch EmbeddingsCode0
3D Medical Image Segmentation based on multi-scale MPU-NetCode0
Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane—0
Abdominal organ segmentation via deep diffeomorphic mesh deformationsCode1
Learnable Weight Initialization for Volumetric Medical Image SegmentationCode1
Continual Learning for Abdominal Multi-Organ and Tumor SegmentationCode1
An image segmentation algorithm based on multi-scale feature pyramid network—0
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three WeeksCode2
Meta-Learners for Few-Shot Weakly-Supervised Medical Image SegmentationCode0
COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training—0
Customized Segment Anything Model for Medical Image SegmentationCode2
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