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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 51–100 of 786 papers

TitleStatusHype
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image SegmentationCode1
Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image SegmentationCode1
Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient NetworkCode1
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck CancerCode1
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CTCode1
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis TasksCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
Continual Learning for Abdominal Multi-Organ and Tumor SegmentationCode1
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via InpaintingCode1
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reportingCode1
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image SegmentationCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image AnalysisCode1
Scratch Each Other's Back: Incomplete Multi-Modal Brain Tumor Segmentation via Category Aware Group Self-Support LearningCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
Learning from partially labeled data for multi-organ and tumor segmentationCode1
Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck CancerCode1
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentationCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT ScansCode1
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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