Published in CTU Digital Library, 2020
This work proposes, implements and evaluates modified convolution operation within CNNs which explicitly considers additional context when processing data.
Recommended citation: Jakub Paplham 2020. Convolutional Neural Networks with Local Context Masks. http://hdl.handle.net/10467/89987
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
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
Graduate Course, Building E, Room KN:102, Thursday 12:45, 2023
Statistical Machine Learning - BE4M33SSU