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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 751–786 of 786 papers

TitleStatusHype
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation—0
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes—0
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Survival Prediction using Radiomics Features—0
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency—0
Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning-based simulation study—0
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
TAGS: 3D Tumor-Adaptive Guidance for SAM—0
DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images—0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation—0
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans—0
Volumetric Attention for 3D Medical Image Segmentation and Detection—0
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)—0
Efficient embedding network for 3D brain tumor segmentation—0
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Brain Tumor Segmentation on MRI with Missing Modalities—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
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