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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 411–420 of 786 papers

TitleStatusHype
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation—0
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network—0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation—0
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation—0
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