SOTAVerified

Tumor Segmentation

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 786 papers

TitleStatusHype
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 ChallengeCode0
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node MetastasisCode0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor SegmentationCode0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology—0
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple HospitalsCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image SegmentationCode1
Segment Anything Model for Brain Tumor Segmentation—0
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction—0
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT ImagesCode0
Towards Optimal Patch Size in Vision Transformers for Tumor SegmentationCode0
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
Tumor-Centered Patching for Enhanced Medical Image Segmentation—0
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification—0
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation—0
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps SegmentationCode0
Automated ensemble method for pediatric brain tumor segmentation—0
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation—0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation—0
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation—0
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative NormalizationCode0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model—0
Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image SegmentationCode1
Confidence Intervals for Performance Estimates in Brain MRI Segmentation—0
Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient NetworkCode1
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
3D Medical Image Segmentation based on multi-scale MPU-NetCode0
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck CancerCode1
Source Identification: A Self-Supervision Task for Dense Prediction—0
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CTCode1
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor SegmentationCode1
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing TasksCode0
Medical Federated Model with Mixture of Personalized and Sharing ComponentsCode0
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationCode2
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images—0
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis TasksCode1
Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging—0
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor SegmentationCode0
Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentation—0
Volumetric medical image segmentation through dual self-distillation in U-shaped networksCode0
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI—0
Show:102550
← PrevPage 6 of 16Next →

No leaderboard results yet.