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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 201–225 of 786 papers

TitleStatusHype
Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality imagesCode0
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy—0
Tumor segmentation on whole slide images: training or prompting?—0
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Self-calibrated convolution towards glioma segmentation—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model—0
CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor SegmentationCode0
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detectionCode0
Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain TumorsCode0
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound DiagnosisCode0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation—0
U-Mamba: Enhancing Long-range Dependency for Biomedical Image SegmentationCode2
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation—0
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy—0
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images—0
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty PredictionCode0
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction—0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations—0
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