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Meinrad Beer on Muck Rack

Meinrad Beer

As seen in: PLOS ONE, Frontiers, MDPI, PeerJ, Academic Radiology
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Articles

FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI

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By Meinrad Beer, Michael Götz Verified, Yiheng Xiong
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (538) Issue with article?

Proto-Caps: interpretable medical image classification using prototype learning and privileged information

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By Catharina Lisson, Timo Ropinski, Meinrad Beer, Luisa Gallée
| PeerJ
We propose a deep neural network for image classification with a specialized architecture that enhances human interpretability by enabling users to understand its information processing. The network uses a hierarchical data process to establish truthful causality between human-defined decision criteria and the final classification.
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Advanced lung imaging with photon-counting detectors: Insights from thermoluminescence dosimetry

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By Henner Huflage, Carsten Hackenbroch, Simone Schüle, Andreas Steven Kunz, Philipp Gruschwitz, Gary Razinskas, Meinrad Beer, Thorsten Alexander Bley, Jan-Peter Grunz, Tobias Wech
| Academic Radiology Verified
This study investigates the dose burden of photon-counting detector (PCD) lung CT with ultra-high-resolution (UHR) and standard mode using organ-based tube current modulation (OBTCM). An anthropomorphic Alderson-Rando phantom was scanned in UHR and standard mode with and without OBTCM on three dose levels (IQ 5, 20, 50).
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