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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 381–390 of 786 papers

TitleStatusHype
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation—0
Belief function-based semi-supervised learning for brain tumor segmentation—0
E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans—0
An Ensemble Approach for Brain Tumor Segmentation and Synthesis—0
BC-MRI-SEG: A Breast Cancer MRI Tumor Segmentation Benchmark—0
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation—0
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation—0
Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model—0
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