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Master Thesis: Detection of Microscopic Fungi and Yeast in Clinical Samples

Published in VISAPP 2023 , Lisbon, Portugal, 2022

This thesis investigates the use of deep neural networks to detect filamentous fungi and yeast in clinical samples.

Recommended citation: Paplhám, J.; Franc, V. and Lžičařová, D. (2023). Detection of Microscopic Fungi and Yeast in Clinical Samples Using Fluorescence Microscopy and Deep Learning. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 777-784. DOI: 10.5220/0011616100003417 http://hdl.handle.net/10467/100872

A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark

Published in ArXiv, 2023

Comparing different age estimation methods poses a challenge due to the unreliability of published results. Previous studies have reported continuous performance improvements using specialized methods; however, our findings challenge these claims.

Recommended citation: Jakub Paplham, and Vojtech Franc 2023. A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark. ArXiv, abs/2307.04570. https://arxiv.org/abs/2307.04570

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