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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 691–700 of 786 papers

TitleStatusHype
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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