{"quality_controlled":"1","main_file_link":[{"open_access":"1"}],"publication_identifier":{"eissn":["2047-217X"]},"citation":{"alphadin":"<span style=\"font-variant:small-caps;\">Shah, Zafran Hussain</span> ; <span style=\"font-variant:small-caps;\">Müller, Marcel</span> ; <span style=\"font-variant:small-caps;\">Hübner, Wolfgang</span> ; <span style=\"font-variant:small-caps;\">Wang, Tung-Cheng</span> ; <span style=\"font-variant:small-caps;\">Telman, Daniel</span> ; <span style=\"font-variant:small-caps;\">Huser, Thomas</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span>: Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data. In: <i>GigaScience</i> Bd. 13, Oxford University Press (OUP) (2024)","chicago":"Shah, Zafran Hussain, Marcel Müller, Wolfgang Hübner, Tung-Cheng Wang, Daniel Telman, Thomas Huser, and Wolfram Schenck. “Evaluation of Swin Transformer and Knowledge Transfer for Denoising of Super-Resolution Structured Illumination Microscopy Data.” <i>GigaScience</i> 13 (2024). <a href=\"https://doi.org/10.1093/gigascience/giad109\">https://doi.org/10.1093/gigascience/giad109</a>.","mla":"Shah, Zafran Hussain, et al. “Evaluation of Swin Transformer and Knowledge Transfer for Denoising of Super-Resolution Structured Illumination Microscopy Data.” <i>GigaScience</i>, vol. 13, Oxford University Press (OUP), 2024, doi:<a href=\"https://doi.org/10.1093/gigascience/giad109\">10.1093/gigascience/giad109</a>.","ieee":"Z. H. Shah <i>et al.</i>, “Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data,” <i>GigaScience</i>, vol. 13, 2024.","short":"Z.H. Shah, M. Müller, W. Hübner, T.-C. Wang, D. Telman, T. Huser, W. Schenck, GigaScience 13 (2024).","bibtex":"@article{Shah_Müller_Hübner_Wang_Telman_Huser_Schenck_2024, title={Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data}, volume={13}, DOI={<a href=\"https://doi.org/10.1093/gigascience/giad109\">10.1093/gigascience/giad109</a>}, journal={GigaScience}, publisher={Oxford University Press (OUP)}, author={Shah, Zafran Hussain and Müller, Marcel and Hübner, Wolfgang and Wang, Tung-Cheng and Telman, Daniel and Huser, Thomas and Schenck, Wolfram}, year={2024} }","apa":"Shah, Z. H., Müller, M., Hübner, W., Wang, T.-C., Telman, D., Huser, T., &#38; Schenck, W. (2024). Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data. <i>GigaScience</i>, <i>13</i>. <a href=\"https://doi.org/10.1093/gigascience/giad109\">https://doi.org/10.1093/gigascience/giad109</a>","ama":"Shah ZH, Müller M, Hübner W, et al. Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data. <i>GigaScience</i>. 2024;13. doi:<a href=\"https://doi.org/10.1093/gigascience/giad109\">10.1093/gigascience/giad109</a>"},"_id":"5500","publication":"GigaScience","year":"2024","status":"public","doi":"10.1093/gigascience/giad109","abstract":[{"lang":"eng","text":"Background: Convolutional neural network (CNN)–based methods have shown excellent performance in denoising and reconstruction of super-resolved structured illumination microscopy (SR-SIM) data. Therefore, CNN-based architectures have been the focus of existing studies. However, Swin Transformer, an alternative and recently proposed deep learning–based image restoration architecture, has not been fully investigated for denoising SR-SIM images. Furthermore, it has not been fully explored how well transfer learning strategies work for denoising SR-SIM images with different noise characteristics and recorded cell structures for these different types of deep learning–based methods. Currently, the scarcity of publicly available SR-SIM datasets limits the exploration of the performance and generalization capabilities of deep learning methods.\r\nResults: In this work, we present SwinT-fairSIM, a novel method based on the Swin Transformer for restoring SR-SIM images with a low signal-to-noise ratio. The experimental results show that SwinT-fairSIM outperforms previous CNN-based denoising methods. Furthermore, as a second contribution, two types of transfer learning—namely, direct transfer and fine-tuning—were benchmarked in combination with SwinT-fairSIM and CNN-based methods for denoising SR-SIM data. Direct transfer did not prove to be a viable strategy, but fine-tuning produced results comparable to conventional training from scratch while saving computational time and potentially reducing the amount of training data required. As a third contribution, we publish four datasets of raw SIM images and already reconstructed SR-SIM images. These datasets cover two different types of cell structures, tubulin filaments and vesicle structures. Different noise levels are available for the tubulin filaments.\r\nConclusion: The SwinT-fairSIM method is well suited for denoising SR-SIM images. By fine-tuning, already trained models can be easily adapted to different noise characteristics and cell structures. Furthermore, the provided datasets are structured in a way that the research community can readily use them for research on denoising, super-resolution, and transfer learning strategies.               "}],"type":"journal_article","date_created":"2025-01-31T17:13:39Z","intvolume":"        13","user_id":"220548","publication_status":"published","oa":"1","author":[{"id":"239296","last_name":"Shah","first_name":"Zafran Hussain","full_name":"Shah, Zafran Hussain"},{"full_name":"Müller, Marcel","first_name":"Marcel","last_name":"Müller"},{"last_name":"Hübner","first_name":"Wolfgang","full_name":"Hübner, Wolfgang"},{"last_name":"Wang","first_name":"Tung-Cheng","full_name":"Wang, Tung-Cheng"},{"first_name":"Daniel","last_name":"Telman","full_name":"Telman, Daniel"},{"first_name":"Thomas","last_name":"Huser","full_name":"Huser, Thomas"},{"first_name":"Wolfram","last_name":"Schenck","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3300-2048/work/177076436","id":"224375","full_name":"Schenck, Wolfram","orcid":"0000-0003-3300-2048"}],"volume":13,"publisher":"Oxford University Press (OUP)","title":"Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data","project":[{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"}],"language":[{"iso":"eng"}],"date_updated":"2025-02-03T07:38:24Z","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"}}