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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 521–530 of 786 papers

TitleStatusHype
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation—0
mlf-core: a framework for deterministic machine learningCode1
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival PredictionCode0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
TransBTS: Multimodal Brain Tumor Segmentation Using TransformerCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
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