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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 121–130 of 786 papers

TitleStatusHype
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationCode1
RFNet: Region-Aware Fusion Network for Incomplete Multi-Modal Brain Tumor SegmentationCode1
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architecturesCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)Code1
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetsCode1
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challengeCode1
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