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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 126–150 of 786 papers

TitleStatusHype
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
High-Resolution Swin Transformer for Automatic Medical Image SegmentationCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
Hybrid Window Attention Based Transformer Architecture for Brain Tumor SegmentationCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
ivadomed: A Medical Imaging Deep Learning ToolboxCode1
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor SegmentationCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor SegmentationCode1
Lesion Focused Super-ResolutionCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image SegmentationCode1
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
Modality-aware Mutual Learning for Multi-modal Medical Image SegmentationCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
Learning from partially labeled data for multi-organ and tumor segmentationCode1
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