SOTAVerified

Segmentation

Papers

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Showing 2501–2525 of 13072 papers

TitleStatusHype
Exploring Optical Flow Inclusion into nnU-Net Framework for Surgical Instrument Segmentation—0
FeatUp: A Model-Agnostic Framework for Features at Any ResolutionCode5
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor SegmentationCode0
Motion-Boundary-Driven Unsupervised Surgical Instrument Segmentation in Low-Quality Optical FlowCode1
MARVIS: Motion & Geometry Aware Real and Virtual Image SegmentationCode0
Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation—0
Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations—0
PosSAM: Panoptic Open-vocabulary Segment AnythingCode2
WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide imagesCode1
When Semantic Segmentation Meets Frequency AliasingCode1
WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity—0
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks—0
Explore In-Context Segmentation via Latent Diffusion Models—0
Task-Specific Adaptation of Segmentation Foundation Model via Prompt Learning—0
Annotation Free Semantic Segmentation with Vision Foundation Models—0
Renovating Names in Open-Vocabulary Segmentation Benchmarks—0
Region-based U-net for accelerated training and enhanced precision in deep brain segmentation—0
Caltech Aerial RGB-Thermal Dataset in the WildCode2
Segmentation of Knee Bones for Osteoarthritis Assessment: A Comparative Analysis of Supervised, Few-Shot, and Zero-Shot Learning Approaches—0
CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions—0
Augmenting Efficient Real-time Surgical Instrument Segmentation in Video with Point Tracking and Segment AnythingCode1
SAMDA: Leveraging SAM on Few-Shot Domain Adaptation for Electronic Microscopy Segmentation—0
An Active Contour Model Driven By the Hybrid Signed Pressure Function—0
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation ModelsCode1
Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ SegmentationCode0
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All datasetsSA-1B.MFSDMMFloodSimGas

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1unSAM+ (Semi-supervised)Average Precision42.8—Unverified
2SAMAverage Precision38.9—Unverified
#ModelMetricClaimedVerifiedStatus
1HNN10%20—Unverified
#ModelMetricClaimedVerifiedStatus
1ABANetF1 Score96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 + DeepLabV3+F1 score0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1LangGasIoU0.69—Unverified