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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 76–100 of 786 papers

TitleStatusHype
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
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
Extending nn-UNet for brain tumor segmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion ModelsCode1
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
High-Resolution Swin Transformer for Automatic Medical Image SegmentationCode1
Hybrid Window Attention Based Transformer Architecture for Brain Tumor SegmentationCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
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