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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 201–210 of 786 papers

TitleStatusHype
AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ SegmentationCode0
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor SegmentationCode0
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor ClassificationCode0
3D U-Net Based Brain Tumor Segmentation and Survival Days PredictionCode0
Brain tumour segmentation using a triplanar ensemble of U-NetsCode0
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Hybrid-Fusion Transformer for Multisequence MRICode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT VolumesCode0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
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