Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation

Alexander Chebykin, Peter Bosman, Tanja Alderliesten
Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, PMLR 250:199-219, 2024.

Abstract

Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security. To get good results, existing methods for medical image synthesis must be manually adjusted when they are applied to unseen data. To remove this manual burden, we introduce a Hyperparameter-Free distributed learning method for automatic medical image Synthesis, Sharing, and Segmentation called HyFree-S3. For three diverse segmentation settings (pelvic MRIs, lung X-rays, polyp photos), the use of HyFree-S3 results in improved performance over training only with site-specific data (in the majority of cases). The hyperparameter-free nature of the method should make data synthesis and sharing easier, potentially leading to an increase in the quantity of available data and consequently the quality of the models trained that may ultimately be applied in the clinic. Our code is available at https://github.com/AwesomeLemon/HyFree-S3

Cite this Paper

BibTeX
@InProceedings{pmlr-v250-chebykin24a, title = {Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation}, author = {Chebykin, Alexander and Bosman, Peter and Alderliesten, Tanja}, booktitle = {Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning}, pages = {199--219}, year = {2024}, editor = {Burgos, Ninon and Petitjean, Caroline and Vakalopoulou, Maria and Christodoulidis, Stergios and Coupe, Pierrick and Delingette, Hervé and Lartizien, Carole and Mateus, Diana}, volume = {250}, series = {Proceedings of Machine Learning Research}, month = {03--05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v250/main/assets/chebykin24a/chebykin24a.pdf}, url = {https://proceedings.mlr.press/v250/chebykin24a.html}, abstract = {Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security. To get good results, existing methods for medical image synthesis must be manually adjusted when they are applied to unseen data. To remove this manual burden, we introduce a Hyperparameter-Free distributed learning method for automatic medical image Synthesis, Sharing, and Segmentation called HyFree-S3. For three diverse segmentation settings (pelvic MRIs, lung X-rays, polyp photos), the use of HyFree-S3 results in improved performance over training only with site-specific data (in the majority of cases). The hyperparameter-free nature of the method should make data synthesis and sharing easier, potentially leading to an increase in the quantity of available data and consequently the quality of the models trained that may ultimately be applied in the clinic. Our code is available at https://github.com/AwesomeLemon/HyFree-S3} }
Endnote
%0 Conference Paper %T Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation %A Alexander Chebykin %A Peter Bosman %A Tanja Alderliesten %B Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ninon Burgos %E Caroline Petitjean %E Maria Vakalopoulou %E Stergios Christodoulidis %E Pierrick Coupe %E Hervé Delingette %E Carole Lartizien %E Diana Mateus %F pmlr-v250-chebykin24a %I PMLR %P 199--219 %U https://proceedings.mlr.press/v250/chebykin24a.html %V 250 %X Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security. To get good results, existing methods for medical image synthesis must be manually adjusted when they are applied to unseen data. To remove this manual burden, we introduce a Hyperparameter-Free distributed learning method for automatic medical image Synthesis, Sharing, and Segmentation called HyFree-S3. For three diverse segmentation settings (pelvic MRIs, lung X-rays, polyp photos), the use of HyFree-S3 results in improved performance over training only with site-specific data (in the majority of cases). The hyperparameter-free nature of the method should make data synthesis and sharing easier, potentially leading to an increase in the quantity of available data and consequently the quality of the models trained that may ultimately be applied in the clinic. Our code is available at https://github.com/AwesomeLemon/HyFree-S3
APA
Chebykin, A., Bosman, P. & Alderliesten, T.. (2024). Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 250:199-219 Available from https://proceedings.mlr.press/v250/chebykin24a.html.

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