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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 776–786 of 786 papers

TitleStatusHype
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networksCode0
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural NetworksCode0
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation—0
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images—0
Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning—0
Within-Brain Classification for Brain Tumor Segmentation—0
Brain Tumor Segmentation: A Comparative Analysis—0
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain—0
Brain Tumor Detection Based On Symmetry Information—0
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