Search Results for author: Axel Saalbach

Found 9 papers, 2 papers with code

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

2 code implementations6 May 2022 Joceline Ziegler, Bjarne Pfitzner, Heinrich Schulz, Axel Saalbach, Bert Arnrich

Both non-private baseline models achieved an area under the ROC curve (AUC) of 0. 94 on the binary classification task of detecting the presence of a medical finding.

Federated Learning Image Reconstruction

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

1 code implementation23 Jan 2020 Evan Schwab, André Gooßen, Hrishikesh Deshpande, Axel Saalbach

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.

General Classification

Smart Chest X-ray Worklist Prioritization using Artificial Intelligence: A Clinical Workflow Simulation

no code implementations23 Jan 2020 Ivo M. Baltruschat, Leonhard Steinmeister, Hannes Nickisch, Axel Saalbach, Michael Grass, Gerhard Adam, Tobias Knopp, Harald Ittrich

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.

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

no code implementations6 Mar 2018 Ivo M. Baltruschat, Hannes Nickisch, Michael Grass, Tobias Knopp, Axel Saalbach

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.

Classification General Classification +1

Can Pretrained Neural Networks Detect Anatomy?

no code implementations18 Dec 2015 Vlado Menkovski, Zharko Aleksovski, Axel Saalbach, Hannes Nickisch

Convolutional neural networks demonstrated outstanding empirical results in computer vision and speech recognition tasks where labeled training data is abundant.

Speech Recognition

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