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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 301–310 of 786 papers

TitleStatusHype
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural NetworksCode0
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image SegmentationCode0
Automatic Brain Tumor Segmentation with Scale Attention NetworkCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural NetworksCode0
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal DependencyCode0
Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor SegmentationCode0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound DiagnosisCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
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