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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 526–550 of 786 papers

TitleStatusHype
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data—0
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessorCode0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
Exploring Feature Representation Learning for Semi-supervised Medical Image SegmentationCode0
Segmentation of Lung Tumor from CT Images using Deep Supervision—0
FedCostWAvg: A new averaging for better Federated Learning—0
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Optimized U-Net for Brain Tumor SegmentationCode0
A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRICode0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3DCode0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images—0
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT—0
Self-supervised Tumor Segmentation through Layer Decomposition—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation—0
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