SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 786 papers

TitleStatusHype
Embracing Massive Medical DataCode1
Medical Image Segmentation Using Squeeze-and-Expansion TransformersCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
Modality-aware Mutual Learning for Multi-modal Medical Image SegmentationCode1
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT ScansCode1
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Continual Learning for Abdominal Multi-Organ and Tumor SegmentationCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple HospitalsCode1
Extending nn-UNet for brain tumor segmentationCode1
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image SegmentationCode1
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetsCode1
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 ChallengeCode1
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentationCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor SegmentationCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion ModelsCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
CANet: Context Aware Network for 3D Brain Glioma 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
Show:102550
← PrevPage 2 of 16Next →

No leaderboard results yet.