SOTAVerified

Medical Image Segmentation

Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

( Image credit: IVD-Net )

Papers

Showing 801–850 of 2089 papers

TitleStatusHype
A Foundation Model for General Moving Object Segmentation in Medical ImagesCode0
PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix AugmentationCode0
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and ClassificationCode0
AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image SegmentationCode0
Confidence Contours: Uncertainty-Aware Annotation for Medical Semantic SegmentationCode0
AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ SegmentationCode0
PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image SegmentationCode0
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac SegmentationCode0
Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium SegmentationCode0
Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image SegmentationCode0
Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging SegmentationCode0
FocusNet: An attention-based Fully Convolutional Network for Medical Image SegmentationCode0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
Opinions Vary? Diagnosis First!Code0
Optimizing Medical Image Segmentation with Advanced Decoder DesignCode0
On the relationship between calibrated predictors and unbiased volume estimationCode0
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point PromptsCode0
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & PracticeCode0
Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image SegmentationCode0
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext TaskCode0
FF-UNet: a U-Shaped Deep Convolutional Neural Network for Multimodal Biomedical Image SegmentationCode0
Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image SegmentationCode0
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and DiceCode0
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural NetworksCode0
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor SegmentationCode0
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging DataCode0
Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon GlandsCode0
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial LearningCode0
Combo Loss: Handling Input and Output Imbalance in Multi-Organ SegmentationCode0
FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image AnalysisCode0
Multi-Task Attention-Based Semi-Supervised Learning for Medical Image SegmentationCode0
Automated Design of Deep Learning Methods for Biomedical Image SegmentationCode0
AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image SegmentationCode0
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation CompletenessCode0
Color-Quality Invariance for Robust Medical Image SegmentationCode0
FedGS: Federated Gradient Scaling for Heterogeneous Medical Image SegmentationCode0
Multi-encoder parse-decoder network for sequential medical image segmentationCode0
Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net) for 3D Medical Image Segmentation and VisualizationCode0
CNN-based Segmentation of Medical Imaging DataCode0
Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-trainingCode0
Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple ratersCode0
CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical ImageCode0
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-RaysCode0
GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image SegmentationCode0
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground TruthCode0
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium SegmentationCode0
MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image SegmentationCode0
FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image SegmentationCode0
Adversarial Robustness Analysis of Vision-Language Models in Medical Image SegmentationCode0
Vision Transformers increase efficiency of 3D cardiac CT multi-label segmentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DUCK-Netmean Dice0.95—Unverified
2EffiSegNet-B5mean Dice0.95—Unverified
3EffiSegNet-B4mean Dice0.95—Unverified
4SegMedmean Dice0.95—Unverified
5FCB Formermean Dice0.94—Unverified
6FCB-SwinV2 Transformermean Dice0.94—Unverified
7SEPmean Dice0.94—Unverified
8LM-Netmean Dice0.94—Unverified
9RAPUNetmean Dice0.94—Unverified
10FCBFormermean Dice0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1DUCK-Netmean Dice0.97—Unverified
2RAPUNetmean Dice0.96—Unverified
3EMCADmean Dice0.95—Unverified
4RaBiTmean Dice0.95—Unverified
5Yolo-SAM 2mean Dice0.95—Unverified
6UGCANetmean Dice0.95—Unverified
7ESFPNet-Lmean Dice0.95—Unverified
8FCBFormermean Dice0.95—Unverified
9DuATmean Dice0.95—Unverified
10SegMedmean Dice0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1RAPUNetmean Dice0.95—Unverified
2DUCK-Netmean Dice0.94—Unverified
3EMCADmean Dice0.92—Unverified
4SegMedmean Dice0.92—Unverified
5UniNetmean Dice0.92—Unverified
6ProMISemean Dice0.87—Unverified
7Meta-Polypmean Dice0.87—Unverified
8ResUNet++ + TTAmean Dice0.85—Unverified
9PVT-GCASCADEmean Dice0.83—Unverified
10PVT-CASCADEmean Dice0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1RAPUNetmean Dice0.96—Unverified
2SegMedmean Dice0.94—Unverified
3DUCK-Netmean Dice0.94—Unverified
4EMCADmean Dice0.92—Unverified
5ProMISemean Dice0.84—Unverified
6RSAFormermean Dice0.84—Unverified
7ESFPNet-Lmean Dice0.82—Unverified
8DuATmean Dice0.82—Unverified
9PVT-CASCADEmean Dice0.8—Unverified
10SSFormer-Lmean Dice0.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Interactive AI-SAM gt boxAvg DSC90.66—Unverified
2Medical SAM AdapterAvg DSC89.8—Unverified
3MedSegDiff-v2Avg DSC89.5—Unverified
4nnUNetAvg DSC88.8—Unverified
5MedNeXt-L (5x5x5)Avg DSC88.76—Unverified
6MISTAvg DSC86.92—Unverified
7nnFormerAvg DSC86.57—Unverified
8AgileFormerAvg DSC86.11—Unverified
9MERITAvg DSC84.9—Unverified
10Automatic AI-SAMAvg DSC84.21—Unverified
#ModelMetricClaimedVerifiedStatus
1FCTAvg DSC94.26—Unverified
2Interactive AI-SAM gt boxAvg DSC93.89—Unverified
3FCTAvg DSC93.02—Unverified
4LHU-NetAvg DSC92.65—Unverified
5MISTAvg DSC92.56—Unverified
6MERITAvg DSC92.32—Unverified
7MERIT-GCASCADEAvg DSC92.23—Unverified
8EMCADAvg DSC92.12—Unverified
9nnFormerAvg DSC92.06—Unverified
10Automatic AI-SAMAvg DSC92.06—Unverified
#ModelMetricClaimedVerifiedStatus
1StardistF184.6—Unverified