Axel Saalbach

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data

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 evaluates the...

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. Especially in the context of chest X-ray classification, results have been reported which...

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. To provide better...

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...

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. In interventional radiology, its use is frequently associated with the need to visualize various tube-like objects, such...

Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs

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. For the task...

When does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification?

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 been developed....

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. In medical imaging, there is a...

Parametrized SOMs for Object Recognition and Pose Estimation

Decomposing the bony thorax in X-ray images

Detection Of Foreign Objects In Chest Radiographs Using Deep Learning

Landmark Constellation Models For Central Venous Catheter Malposition Detection

When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification?

Deep Learning Based Segmentation of Body Parts in CT Localizers and Application to Scan Planning

Automated anatomy detection in CT localizer images

Localization of Critical Findings in Chest X-Ray Without Local Annotations Using Multi-Instance Learning

Foveal fully convolutional nets for multi-organ segmentation

Multiscale entropy analysis in dynamic contrast-enhanced MRI