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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

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Showing 51–75 of 786 papers

TitleStatusHype
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings—0
A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor SegmentationCode0
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation—0
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning—0
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths—0
Hybridization of Attention UNet with Repeated Atrous Spatial Pyramid Pooling for Improved Brain Tumour Segmentation—0
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation—0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Vision Foundation Models for Computed TomographyCode2
Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation—0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRICode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body ImagingCode2
Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model—0
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities—0
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor SegmentationCode1
SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor SegmentationCode0
Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment—0
A fuzzy rank-based ensemble of CNN models for MRI segmentationCode0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Text-Driven Tumor Synthesis—0
PINN-EMFNet: PINN-based and Enhanced Multi-Scale Feature Fusion Network for Breast Ultrasound Images Segmentation—0
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