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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 701–710 of 786 papers

TitleStatusHype
Brain Tumor Segmentation on MRI with Missing Modalities—0
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRICode0
Towards annotation-efficient segmentation via image-to-image translation—0
The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical OutcomesCode0
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation—0
Deep segmentation networks predict survival of non-small cell lung cancer—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
The Liver Tumor Segmentation Benchmark (LiTS)Code1
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