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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 611–620 of 786 papers

TitleStatusHype
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
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
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
Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image SynthesisCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
Joint Liver Lesion Segmentation and Classification via Transfer Learning—0
Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor SegmentationCode1
Decentralized Differentially Private Segmentation with PATE—0
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