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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
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor SegmentationCode0
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation—0
Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss functionCode0
Brain Tumor Detection using Convolutional Neural NetworkCode0
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow—0
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
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