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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 721–730 of 786 papers

TitleStatusHype
MRI Tumor Segmentation with Densely Connected 3D CNNCode0
MSTT-199: MRI Dataset for Musculoskeletal Soft Tissue Tumor SegmentationCode0
Multi-channel MRI Embedding: An EffectiveStrategy for Enhancement of Human Brain WholeTumor SegmentationCode0
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection ClassifierCode0
UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature FusionCode0
RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray ProjectionCode0
AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor Lesion Based on Deep Learning and FDG PET/CTCode0
Whole-brain radiomics for clustered federated personalization in brain tumor segmentationCode0
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor SegmentationCode0
A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRICode0
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