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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 161–170 of 786 papers

TitleStatusHype
Medical Image Segmentation Using Squeeze-and-Expansion TransformersCode1
RFNet: Region-Aware Fusion Network for Incomplete Multi-Modal Brain Tumor SegmentationCode1
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning—0
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks—0
Automated Tumor Segmentation and Brain Mapping for the Tumor Area—0
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images—0
Automated Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy from DWI Data—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
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