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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 731–740 of 786 papers

TitleStatusHype
Glioma Segmentation with Cascaded UnetCode0
Co-Learning Feature Fusion Maps from PET-CT Images of Lung CancerCode0
Survival prediction using ensemble tumor segmentation and transfer learningCode0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural NetworkCode0
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation—0
Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation—0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks—0
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