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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
Extending nn-UNet for brain tumor segmentationCode1
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
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)Code1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor SegmentationCode1
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image SegmentationCode1
Embracing Massive Medical DataCode1
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
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetsCode1
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
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
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
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
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
FIBA: Frequency-Injection based Backdoor Attack in Medical Image AnalysisCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
3D Self-Supervised Methods for Medical ImagingCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
High-Resolution Swin Transformer for Automatic Medical Image SegmentationCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion ModelsCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
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