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 701–725 of 786 papers

TitleStatusHype
Brain Tumor Segmentation on MRI with Missing Modalities—0
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRICode0
Towards annotation-efficient segmentation via image-to-image translation—0
The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical OutcomesCode0
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation—0
Deep segmentation networks predict survival of non-small cell lung cancer—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
The Liver Tumor Segmentation Benchmark (LiTS)Code1
Cascaded V-Net using ROI masks for brain tumor segmentation—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scansCode0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation—0
Lesion Focused Super-ResolutionCode1
Show:102550
← PrevPage 29 of 32Next →

No leaderboard results yet.