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Found 10 papers, 2 papers with code
Bottom-Up Instance Segmentation of Catheters for Chest X-Rays
Chest X-ray (CXR) is frequently employed in emergency departments and intensive care units to verify the proper placement of central lines and tubes and to rule out related complications.
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data
We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients.
Tubular Shape Aware Data Generation for Semantic Segmentation in Medical Imaging
Chest X-ray is one of the most widespread examinations of the human body.
Smart Chest X-ray Worklist Prioritization using Artificial Intelligence: A Clinical Workflow Simulation
Our simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO.
Localization of Critical Findings in Chest X-Ray without Local Annotations Using Multi-Instance Learning
The automatic detection of critical findings in chest X-rays (CXR), such as pneumothorax, is important for assisting radiologists in their clinical workflow like triaging time-sensitive cases and screening for incidental findings.
Continual Learning for Domain Adaptation in Chest X-ray Classification
Over the last years, Deep Learning has been successfully applied to a broad range of medical applications.
Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs
Pneumothorax is a critical condition that requires timely communication and immediate action.
When does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification?
Chest radiography is the most common clinical examination type.
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
The increased availability of X-ray image archives (e. g. the ChestX-ray14 dataset from the NIH Clinical Center) has triggered a growing interest in deep learning techniques.
Can Pretrained Neural Networks Detect Anatomy?
Convolutional neural networks demonstrated outstanding empirical results in computer vision and speech recognition tasks where labeled training data is abundant.
