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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 191–200 of 786 papers

TitleStatusHype
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net—0
Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma—0
QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter—0
Towards Universal Text-driven CT Image SegmentationCode0
Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor SegmentationCode0
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation—0
Clinical Inspired MRI Lesion Segmentation—0
Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
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