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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 71–80 of 786 papers

TitleStatusHype
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
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