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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 101–125 of 786 papers

TitleStatusHype
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and AnalysisCode1
Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor SegmentationCode1
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and PediatricsCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetsCode1
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image SegmentationCode1
PRISM: A Promptable and Robust Interactive Segmentation Model with Visual PromptsCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck CancerCode1
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple HospitalsCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
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