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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 41–50 of 786 papers

TitleStatusHype
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation—0
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI DataCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
Clinical Inspired MRI Lesion Segmentation—0
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
Triad: Vision Foundation Model for 3D Magnetic Resonance ImagingCode1
Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
Position Paper: Building Trust in Synthetic Data for Clinical AI—0
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