18 citations · 50 across the 10 of their papers we have counts for
11 papers
Bottom-Up Instance Segmentation of Catheters for Chest X-Rays
Francesca Boccardi, Axel Saalbach, Heinrich Schulz +2
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 complic…
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…
Tubular Shape Aware Data Generation for Semantic Segmentation in Medical Imaging
Ilyas Sirazitdinov, Heinrich Schulz, Axel Saalbach +2
Chest X-ray is one of the most widespread examinations of the human body. In interventional radiology, its use is frequently associated with the need to visualize various tube-like…
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…
When does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification?
Ivo M. Baltruschat, Leonhard Steinmeister, Harald Ittrich +6
Chest radiography is the most common clinical examination type. To improve the quality of patient care and to reduce workload, methods for automatic pathology classification have b…
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
Ivo M. Baltruschat, Hannes Nickisch, Michael Grass +2
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. To pro…
Can Pretrained Neural Networks Detect Anatomy?
Vlado Menkovski, Zharko Aleksovski, Axel Saalbach +1
Convolutional neural networks demonstrated outstanding empirical results in computer vision and speech recognition tasks where labeled training data is abundant. In medical imaging…