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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
Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition—0
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance ImagingCode0
AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ SegmentationCode0
Cross-Organ Domain Adaptive Neural Network for Pancreatic Endoscopic Ultrasound Image Segmentation—0
MSTT-199: MRI Dataset for Musculoskeletal Soft Tissue Tumor SegmentationCode0
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection—0
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection—0
Exploring Adult Glioma through MRI: A Review of Publicly Available Datasets to Guide Efficient Image Analysis—0
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities—0
Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images—0
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