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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
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
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
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck CancerCode1
mlf-core: a framework for deterministic machine learningCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNetCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
Continual Learning for Abdominal Multi-Organ and Tumor SegmentationCode1
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image SegmentationCode1
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor SegmentationCode1
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image SegmentationCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple HospitalsCode1
3D Self-Supervised Methods for Medical ImagingCode1
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)Code1
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT ScansCode1
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image SegmentationCode1
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
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
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
ivadomed: A Medical Imaging Deep Learning ToolboxCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
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