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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 476–500 of 786 papers

TitleStatusHype
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck CancersCode0
Automated head and neck tumor segmentation from 3D PET/CT—0
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images—0
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection ClassifierCode0
AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor Lesion Based on Deep Learning and FDG PET/CTCode0
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—0
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
Large-Kernel Attention for 3D Medical Image Segmentation—0
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images—0
Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images—0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
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