2024-11-17-ben-david24a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Deep-Learning Based Virtual Stain Multiplexing Immunohistochemistry Slides – a Pilot Study	Proceedings of the MICCAI Workshop on Computational Pathology	In this paper, we introduce a novel deep-learning based method for virtual stain multiplexing of immunohistochemistry (IHC) stains. Traditional IHC techniques generally involve a single stain that highlights a single target protein, but this can be enriched with stain multiplexing. Our proposed method leverages sequential staining to train a model to virtually stain multiplex additional IHC on top of a digitally scanned whole slide image (WSI), without requiring a complex setup or any additional tissue sections and stains. To this end, we designed a novel model architecture, guided by the physical sequential staining process which provides superior performance. The model was optimized using a custom loss function that combines mean squared error (MSE) with semantic information, allowing the model to focus on learning the relevant differences between the input and ground truth. As an example application, we consider the problem of detecting macro-phages on PD-L1 IHC 22C3 pharmDx NSCLC WSIs. We demonstrated virtual stain multiplexing CD68 on top of PD-L1 22C3 pharmDx stained slides, which helps to detect macrophages and distinguish them from PD-L1+ tumor cells, which are often visually similar. Our pilot-study results showed significant improvement in a pathologist’s ability to distinguish macrophages when using the virtually stain multiplexed CD68 decision supporting layer.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	ben-david24a	0	Deep-Learning Based Virtual Stain Multiplexing Immunohistochemistry Slides – a Pilot Study	107	120	107-120	107	false	Ben-David, Oded and Arbel, Elad and Rabkin, Daniela and Remer, Itay and Ben-Dor, Amir and Aviel-Ronen, Sarit and Aidt, Frederik and Hagedorn-Olsen, Tine and Jacobsen, Lars and Kersch, Kristopher and Tsalenko, Anya	given family Oded Ben-David given family Elad Arbel given family Daniela Rabkin given family Itay Remer given family Amir Ben-Dor given family Sarit Aviel-Ronen given family Frederik Aidt given family Tine Hagedorn-Olsen given family Lars Jacobsen given family Kristopher Kersch given family Anya Tsalenko	2024-11-17		Proceedings of the MICCAI Workshop on Computational Pathology	254	inproceedings	date-parts 2024 11 17	https://raw.githubusercontent.com/mlresearch/v254/main/assets/ben-david24a/ben-david24a.pdf