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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 521–530 of 786 papers

TitleStatusHype
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI—0
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark—0
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking ResultsCode0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data—0
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessorCode0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
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