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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 81–90 of 786 papers

TitleStatusHype
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetsCode1
3D Self-Supervised Methods for Medical ImagingCode1
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple HospitalsCode1
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentationCode1
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT ScansCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
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