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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 281–290 of 786 papers

TitleStatusHype
Confidence Intervals for Performance Estimates in Brain MRI Segmentation—0
Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient NetworkCode1
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
3D Medical Image Segmentation based on multi-scale MPU-NetCode0
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck CancerCode1
Source Identification: A Self-Supervision Task for Dense Prediction—0
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CTCode1
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor SegmentationCode1
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing TasksCode0
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
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