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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 201–210 of 786 papers

TitleStatusHype
Position Paper: Building Trust in Synthetic Data for Clinical AI—0
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
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning—0
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation—0
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
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation—0
Hybridization of Attention UNet with Repeated Atrous Spatial Pyramid Pooling for Improved Brain Tumour Segmentation—0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation—0
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