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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 676–700 of 786 papers

TitleStatusHype
3D Medical Image Segmentation based on multi-scale MPU-NetCode0
Autofocus Layer for Semantic SegmentationCode0
Glioma Segmentation with Cascaded UnetCode0
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor SegmentationCode0
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty PredictionCode0
Glioblastoma Tumor Segmentation using an Ensemble of Vision TransformersCode0
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor ClassificationCode0
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival PredictionCode0
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival PredictionCode0
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scansCode0
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation NetworkCode0
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation ProblemCode0
Brain Tumor Detection using Convolutional Neural NetworkCode0
Medical Federated Model with Mixture of Personalized and Sharing ComponentsCode0
Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound ImagesCode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck CancersCode0
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor SegmentationCode0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor SegmentationCode0
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?Code0
Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma SegmentationCode0
Towards Optimal Patch Size in Vision Transformers for Tumor SegmentationCode0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
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