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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
Hybrid Window Attention Based Transformer Architecture for Brain Tumor SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
Rethinking the Unpretentious U-net for Medical Ultrasound Image SegmentationCode1
TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival PredictionCode1
NestedFormer: Nested Modality-Aware Transformer for Brain Tumor SegmentationCode1
SFusion: Self-attention based N-to-One Multimodal Fusion BlockCode1
PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge DistillationCode1
High-Resolution Swin Transformer for Automatic Medical Image SegmentationCode1
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
TBraTS: Trusted Brain Tumor SegmentationCode1
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
FedMix: Mixed Supervised Federated Learning for Medical Image SegmentationCode1
Preoperative brain tumor imaging: models and software for segmentation and standardized reportingCode1
Multi-View Hypercomplex Learning for Breast Cancer ScreeningCode1
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalitiesCode1
Translation Consistent Semi-supervised Segmentation for 3D Medical ImagesCode1
Self Pre-training with Masked Autoencoders for Medical Image Classification and SegmentationCode1
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image SegmentationCode1
Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation TaskCode1
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT ImagesCode1
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challengeCode1
Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled DataCode1
Teacher-Student Architecture for Mixed Supervised Lung Tumor SegmentationCode1
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
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