Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models
Abstract High-quality image data is essential for training deep learning (DL) classifiers, yet data sharing is often limited by privacy concerns. We hypothesized that generative adversarial networks (GANs) could synthesize bone marrow smear (BMS) images suitable for classifier training. BMS from 125...
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Nature Portfolio
2025-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01563-9 |
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| author | Jan-Niklas Eckardt Ishan Srivastava Zizhe Wang Susann Winter Tim Schmittmann Sebastian Riechert Miriam Eva Helena Gediga Anas Shekh Sulaiman Martin M. K. Schneider Freya Schulze Christian Thiede Katja Sockel Frank Kroschinsky Christoph Röllig Martin Bornhäuser Karsten Wendt Jan Moritz Middeke |
| author_facet | Jan-Niklas Eckardt Ishan Srivastava Zizhe Wang Susann Winter Tim Schmittmann Sebastian Riechert Miriam Eva Helena Gediga Anas Shekh Sulaiman Martin M. K. Schneider Freya Schulze Christian Thiede Katja Sockel Frank Kroschinsky Christoph Röllig Martin Bornhäuser Karsten Wendt Jan Moritz Middeke |
| author_sort | Jan-Niklas Eckardt |
| collection | DOAJ |
| description | Abstract High-quality image data is essential for training deep learning (DL) classifiers, yet data sharing is often limited by privacy concerns. We hypothesized that generative adversarial networks (GANs) could synthesize bone marrow smear (BMS) images suitable for classifier training. BMS from 1251 patients with acute myeloid leukemia (AML), 51 patients with acute promyelocytic leukemia (APL), and 236 stem cell donors were digitized, and synthetic images were generated using StyleGAN2-Ada. In a blinded visual Turing test, eight hematologists achieved 63% accuracy in identifying synthetic images, confirming high image quality. DL classifiers trained on real data achieved AUROCs of 0.99 across AML, APL, and donor classifications, with performance remaining above 0.95 even when incrementally substituting real data for synthetic samples. Adding synthetic data to real training data offered performance gains for an exceptionally rare disease (APL). Our study demonstrates the usability of synthetic BMS data for training highly accurate image classifiers in microscopy. |
| format | Article |
| id | doaj-art-1dc7ce8daaba4825b86ff26797334cf1 |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-1dc7ce8daaba4825b86ff26797334cf12025-08-20T02:52:19ZengNature Portfolionpj Digital Medicine2398-63522025-03-018111010.1038/s41746-025-01563-9Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification modelsJan-Niklas Eckardt0Ishan Srivastava1Zizhe Wang2Susann Winter3Tim Schmittmann4Sebastian Riechert5Miriam Eva Helena Gediga6Anas Shekh Sulaiman7Martin M. K. Schneider8Freya Schulze9Christian Thiede10Katja Sockel11Frank Kroschinsky12Christoph Röllig13Martin Bornhäuser14Karsten Wendt15Jan Moritz Middeke16Department of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyChair of Software Technology, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyChair of Software Technology, TUD Dresden University of TechnologyElse Kröner Fresenius Center for Digital Health, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyChair of Software Technology, TUD Dresden University of TechnologyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyAbstract High-quality image data is essential for training deep learning (DL) classifiers, yet data sharing is often limited by privacy concerns. We hypothesized that generative adversarial networks (GANs) could synthesize bone marrow smear (BMS) images suitable for classifier training. BMS from 1251 patients with acute myeloid leukemia (AML), 51 patients with acute promyelocytic leukemia (APL), and 236 stem cell donors were digitized, and synthetic images were generated using StyleGAN2-Ada. In a blinded visual Turing test, eight hematologists achieved 63% accuracy in identifying synthetic images, confirming high image quality. DL classifiers trained on real data achieved AUROCs of 0.99 across AML, APL, and donor classifications, with performance remaining above 0.95 even when incrementally substituting real data for synthetic samples. Adding synthetic data to real training data offered performance gains for an exceptionally rare disease (APL). Our study demonstrates the usability of synthetic BMS data for training highly accurate image classifiers in microscopy.https://doi.org/10.1038/s41746-025-01563-9 |
| spellingShingle | Jan-Niklas Eckardt Ishan Srivastava Zizhe Wang Susann Winter Tim Schmittmann Sebastian Riechert Miriam Eva Helena Gediga Anas Shekh Sulaiman Martin M. K. Schneider Freya Schulze Christian Thiede Katja Sockel Frank Kroschinsky Christoph Röllig Martin Bornhäuser Karsten Wendt Jan Moritz Middeke Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models npj Digital Medicine |
| title | Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models |
| title_full | Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models |
| title_fullStr | Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models |
| title_full_unstemmed | Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models |
| title_short | Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models |
| title_sort | synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models |
| url | https://doi.org/10.1038/s41746-025-01563-9 |
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