SOTAVerified

Image Segmentation

Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.

Papers

Showing 176–200 of 5035 papers

TitleStatusHype
PathSeqSAM: Sequential Modeling for Pathology Image Segmentation with SAM2Code0
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention—0
A Unified Loss for Handling Inter-Class and Intra-Class Imbalance in Medical Image SegmentationCode0
Do Segmentation Models Understand Vascular Structure? A Blob-Based XAI Framework—0
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data—0
SydneyScapes: Image Segmentation for Australian Environments—0
Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement—0
Conditional Conformal Risk Adaptation—0
PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development—0
Zeus: Zero-shot LLM Instruction for Union Segmentation in Multimodal Medical Imaging—0
CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images—0
Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature FusionCode0
HRMedSeg: Unlocking High-resolution Medical Image segmentation via Memory-efficient Attention ModelingCode1
MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image SegmentationCode0
SlicerNNInteractive: A 3D Slicer extension for nnInteractiveCode2
Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors—0
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation—0
View2CAD: Reconstructing View-Centric CAD Models from Single RGB-D Scans—0
Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan—0
Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
GraphSeg: Segmented 3D Representations via Graph Edge Addition and ContractionCode0
Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing—0
Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation—0
SelfMedHPM: Self Pre-training With Hard Patches Mining Masked Autoencoders For Medical Image Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SAM2-UNetIoU0.92—Unverified
2HetNetIoU0.83—Unverified
3PMDIoU0.82—Unverified
4SANetIoU0.8—Unverified
5MirrorNetIoU0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1SAM2-UNetIoU0.73—Unverified
2HetNetIoU0.69—Unverified
3SANetIoU0.67—Unverified
4PMDIoU0.66—Unverified
5MirrorNetIoU0.59—Unverified
#ModelMetricClaimedVerifiedStatus
1SAM2-UNetmIoU0.8—Unverified
2MAS-SAMmIoU0.79—Unverified
3MASNetmIoU0.74—Unverified
4ZoomNetmIoU0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1HIPIE (ViT-H)mIoUPartS63.8—Unverified
2PPSmIoUPartS58.6—Unverified
3HIPIE (ResNet-50)mIoUPartS57.2—Unverified
4JPPFmIoUPartS54.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MAS-SAMmIoU0.74—Unverified
2SAM2-UNetmIoU0.74—Unverified
3MASNetmIoU0.73—Unverified
4ZoomNetmIoU0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4-CmIoU63.6—Unverified
2OneNete,4-SmAP0.552.75—Unverified
3OneNeted,4mIoU14.9—Unverified
#ModelMetricClaimedVerifiedStatus
1UNetRDice0.98—Unverified
2PALEDDice0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1ResAttUNetIoU0.67—Unverified
2UNetIoU0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1SynCo (ResNet-50) 200epmask AP35.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MobileOne-S0GFLOPs0.28—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4mIoU6.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4-CDice Score0.97—Unverified