Five Pitfalls When Assessing Synthetic Medical Images with Reference Metrics

Autor: Dohmen, Melanie, Truong, Tuan, Baltruschat, Ivo M., Lenga, Matthias

Publikováno v: In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Mehrof, D., Yuan, Y. (eds) Deep Generative Models. DGM4MICCAI 2024. Lecture Notes in Computer Science, vol 15224. Springer, Cham

Reference metrics have been developed to objectively and quantitatively compare two images. Especially for evaluating the quality of reconstructed or compressed images, these metrics have shown very useful. Extensive tests of such metrics on benchmar

Externí odkaz: http://arxiv.org/abs/2408.06075

Uncertainty Estimation in Contrast-Enhanced MR Image Translation with Multi-Axis Fusion

Autor: Baltruschat, Ivo M., Janbakhshi, Parvaneh, Dohmen, Melanie, Lenga, Matthias

In recent years, deep learning has been applied to a wide range of medical imaging and image processing tasks. In this work, we focus on the estimation of epistemic uncertainty for 3D medical image-to-image translation. We propose a novel model uncer

Externí odkaz: http://arxiv.org/abs/2311.12153

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

Autor: Baltruschat, Ivo M., Steinmeister, Leonhard, Nickisch, Hannes, Saalbach, Axel, Grass, Michael, Adam, Gerhard, Knopp, Tobias, Ittrich, Harald

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 critical findings in chest radiographs (CXRs). Furthermore, we investigate

Externí odkaz: http://arxiv.org/abs/2001.08625

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

Autor: Baltruschat, Ivo M., Nickisch, Hannes, Grass, Michael, Knopp, Tobias, Saalbach, Axel

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 insight into the different approaches, and their applicati

Externí odkaz: http://arxiv.org/abs/1803.02315

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

Autor: Baltruschat, Ivo M., Nickisch, Hannes, Grass, Michael, Knopp, Tobias, Saalbach, Axel

Publikováno v: Scientific Reports
Scientific reports 1 (9): 6381 (2019-04-23)
Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)

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 insight into the different approaches, and their applicati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd380d5a946b89e3a3b73b1e99e1ecbb
http://europepmc.org/articles/PMC6476887