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 176–200 of 786 papers

TitleStatusHype
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks—0
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling—0
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation—0
Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation—0
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation—0
Decentralized Differentially Private Segmentation with PATE—0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
A Hybrid Framework for Tumor Saliency Estimation—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
A Structural Graph-Based Method for MRI Analysis—0
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
Show:102550
← PrevPage 8 of 32Next →

No leaderboard results yet.