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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 631–640 of 786 papers

TitleStatusHype
Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet—0
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation—0
Joint Liver Lesion Segmentation and Classification via Transfer Learning—0
Decentralized Differentially Private Segmentation with PATE—0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
Volumetric Attention for 3D Medical Image Segmentation and Detection—0
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
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