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Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
Pre-trained models, e.g., from ImageNet, have proven to be effective in ...
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DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
Recently, neural architecture search (NAS) has been applied to automatic...
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Test-Time Training for Deformable Multi-Scale Image Registration
Registration is a fundamental task in medical robotics and is often a cr...
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UNETR: Transformers for 3D Medical Image Segmentation
Fully Convolutional Neural Networks (FCNNs) with contracting and expansi...
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Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
Active learning is a unique abstraction of machine learning techniques w...
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Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19
Previous work established skip-gram word2vec models could be used to min...
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Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
The recent outbreak of COVID-19 has led to urgent needs for reliable dia...
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Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
The performance of deep learning-based methods strongly relies on the nu...
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Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning
The training of deep learning models typically requires extensive data, ...
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Going to Extremes: Weakly Supervised Medical Image Segmentation
Medical image annotation is a major hurdle for developing precise and ro...
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Learning Image Labels On-the-fly for Training Robust Classification Models
Current deep learning paradigms largely benefit from the tremendous amou...
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Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
CXRs are a crucial and extraordinarily common diagnostic tool, leading t...
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Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
Detecting clinically relevant objects in medical images is a challenge d...
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Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking adv...
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Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Although having achieved great success in medical image segmentation, de...
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LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
Deep Learning (DL) models are becoming larger, because the increase in m...
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Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
Deep neural network (DNN) based approaches have been widely investigated...
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Enhancing Foreground Boundaries for Medical Image Segmentation
Object segmentation plays an important role in the modern medical image ...
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When Radiology Report Generation Meets Knowledge Graph
Automatic radiology report generation has been an attracting research pr...
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VerSe: A Vertebrae Labelling and Segmentation Benchmark
In this paper we report the challenge set-up and results of the Large Sc...
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C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
3D convolution neural networks (CNN) have been proved very successful in...
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End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Automatic segmentation of abdomen organs using medical imaging has many ...
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NeurReg: Neural Registration and Its Application to Image Segmentation
Registration is a fundamental task in medical image analysis which can b...
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Cardiac Segmentation of LGE MRI with Noisy Labels
In this work, we attempt the segmentation of cardiac structures in late ...
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Weakly supervised segmentation from extreme points
Annotation of medical images has been a major bottleneck for the develop...
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Privacy-preserving Federated Brain Tumour Segmentation
Due to medical data privacy regulations, it is often infeasible to colle...
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Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Radiogenomic map linking image features and gene expression profiles is ...
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Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking
Echocardiography has become routinely used in the diagnosis of cardiomyo...
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4D CNN for semantic segmentation of cardiac volumetric sequences
We propose a 4D convolutional neural network (CNN) for the segmentation ...
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When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
Recent advances in deep learning for medical image segmentation demonstr...
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V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation
Deep learning algorithms, in particular 2D and 3D fully convolutional ne...
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An Alarm System For Segmentation Algorithm Based On Shape Model
It is usually hard for a learning system to predict correctly on rare ev...
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Interactive segmentation of medical images through fully convolutional neural networks
Image segmentation plays an essential role in medicine for both diagnost...
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3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
We propose a novel framework, uncertainty-aware multi-view co-training (...
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Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
Simultaneous segmentation of multiple organs from different medical imag...
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3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
While deep convolutional neural networks (CNN) have been successfully ap...
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Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many c...
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Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
Automatic localization and labeling of vertebra in 3D medical images pla...
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