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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 261–270 of 786 papers

TitleStatusHype
Glioblastoma Tumor Segmentation using an Ensemble of Vision TransformersCode0
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound DiagnosisCode0
A New Logic For Pediatric Brain Tumor SegmentationCode0
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor SegmentationCode0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
Glioma Segmentation with Cascaded UnetCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing TasksCode0
A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging SegmentationCode0
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