8 papers
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data
Joceline Ziegler, Bjarne Pfitzner, Heinrich Schulz +2
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper eval…
Localization of Critical Findings in Chest X-Ray without Local Annotations Using Multi-Instance Learning
Evan Schwab, André Gooßen, Hrishikesh Deshpande +1
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-sens…
Continual Learning for Domain Adaptation in Chest X-ray Classification
Matthias Lenga, Heinrich Schulz, Axel Saalbach
Over the last years, Deep Learning has been successfully applied to a broad range of medical applications. Especially in the context of chest X-ray classification, results have bee…
Smart Chest X-ray Worklist Prioritization using Artificial Intelligence: A Clinical Workflow Simulation
Ivo M. Baltruschat, Leonhard Steinmeister, Hannes Nickisch +5
The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTAT) for criti…
Sequential Rib Labeling and Segmentation in Chest X-Ray using Mask R-CNN
Jöran Wessel, Mattias P. Heinrich, Jens von Berg +2
Mask R-CNN is a state-of-the-art network architecture for the detection and segmentation of object instances in the computer vision domain. In this contribution, it is used to loca…
Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs
André Gooßen, Hrishikesh Deshpande, Tim Harder +5
Pneumothorax is a critical condition that requires timely communication and immediate action. In order to prevent significant morbidity or patient death, early detection is crucial…