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Tumor Segmentation

10-shot image generation › Semantic Segmentation › Tumor Segmentation 5 평가 기록 4 datasets

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.

논문

Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images

1910.08978 2019-10-20 이 페이지의 평가 1건

Dice Score: 0.7341

Label-Free Liver Tumor Segmentation

2303.14869 2023-03-27 이 페이지의 평가 1건

Dice: 59.77NSD: 61.29

Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

2006.15954 2020-06-29 이 페이지의 평가 1건

DSC: 0.8075

The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography

2406.05231 2024-06-07 이 페이지의 평가 1건

ChallengeScore: 0.729Average Dice: 0.703 ± 0.240Long-Axis SMAPE: 0.112Short-Axis SMAPE: 0.120

U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

2401.04722 2024-01-09 이 페이지의 평가 1건

ChallengeScore: 0.735Average Dice: 0.708 ± 0.235Long-Axis SMAPE: 0.103Short-Axis SMAPE: 0.118