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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 251–260 of 786 papers

TitleStatusHype
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using MRI tumor annotationsCode0
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor SegmentationCode0
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete ModalitiesCode0
SegAN: Adversarial Network with Multi-scale L_1 Loss for Medical Image SegmentationCode0
Glioblastoma Tumor Segmentation using an Ensemble of Vision TransformersCode0
Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided RadiotherapyCode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
GuideGen: A Text-Guided Framework for Full-torso Anatomy and CT Volume GenerationCode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation NetworkCode0
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