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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 751–760 of 786 papers

TitleStatusHype
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features—0
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images—0
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans—0
MRI Tumor Segmentation with Densely Connected 3D CNNCode0
2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation—0
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice LossCode0
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning—0
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic VolumesCode0
Segmenting Brain Tumors with Symmetry—0
Automated Tumor Segmentation and Brain Mapping for the Tumor Area—0
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