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 376–400 of 786 papers

TitleStatusHype
Class Balanced PixelNet for Neurological Image Segmentation—0
Clinical Inspired MRI Lesion Segmentation—0
Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images—0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—0
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images—0
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images—0
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation—0
Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentation—0
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities—0
Confidence Intervals for Performance Estimates in Brain MRI Segmentation—0
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network—0
Context Aware 3D UNet for Brain Tumor Segmentation—0
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models—0
Cross-Organ Domain Adaptive Neural Network for Pancreatic Endoscopic Ultrasound Image Segmentation—0
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
Show:102550
← PrevPage 16 of 32Next →

No leaderboard results yet.