Digitally Stained Confocal Microscopy through Deep Learning

Marc Combalia, Javiera Pérez-Anker, Adriana García-Herrera, Llúcia Alos, Verónica Vilaplana, Ferran Marqués, Susana Puig, Josep Malvehy
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:121-129, 2019.

Abstract

Specialists have used confocal microscopy in the ex-vivo modality to identify Basal Cell Carcinoma tumors with an overall sensitivity of 96.6% and specificity of 89.2% {{Chung et al.}} ({2004}). However, this technology hasn’t established yet in the standard clinical practice because most pathologists lack the knowledge to interpret its output. In this paper we propose a combination of deep learning and computer vision techniques to digitally stain confocal microscopy images into H&E-like slides, enabling pathologists to interpret these images without specific training. We use a fully convolutional neural network with a multiplicative residual connection to denoise the confocal microscopy images, and then stain them using a Cycle Consistency Generative Adversarial Network.

Cite this Paper

BibTeX
@InProceedings{pmlr-v102-combalia19a, title = {Digitally Stained Confocal Microscopy through Deep Learning}, author = {Combalia, Marc and {P\'erez-Anker}, Javiera and {Garc\'ia-Herrera}, Adriana and Alos, {Ll\'ucia} and Vilaplana, {Ver\'onica} and {Marqu\'es}, Ferran and Puig, Susana and Malvehy, Josep}, booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning}, pages = {121--129}, year = {2019}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, volume = {102}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v102/combalia19a/combalia19a.pdf}, url = {https://proceedings.mlr.press/v102/combalia19a.html}, abstract = {Specialists have used confocal microscopy in the ex-vivo modality to identify Basal Cell Carcinoma tumors with an overall sensitivity of 96.6% and specificity of 89.2% {{Chung et al.}} ({2004}). However, this technology hasn’t established yet in the standard clinical practice because most pathologists lack the knowledge to interpret its output. In this paper we propose a combination of deep learning and computer vision techniques to digitally stain confocal microscopy images into H&E-like slides, enabling pathologists to interpret these images without specific training. We use a fully convolutional neural network with a multiplicative residual connection to denoise the confocal microscopy images, and then stain them using a Cycle Consistency Generative Adversarial Network.} }
Endnote
%0 Conference Paper %T Digitally Stained Confocal Microscopy through Deep Learning %A Marc Combalia %A Javiera Pérez-Anker %A Adriana García-Herrera %A Llúcia Alos %A Verónica Vilaplana %A Ferran Marqués %A Susana Puig %A Josep Malvehy %B Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2019 %E M. Jorge Cardoso %E Aasa Feragen %E Ben Glocker %E Ender Konukoglu %E Ipek Oguz %E Gozde Unal %E Tom Vercauteren %F pmlr-v102-combalia19a %I PMLR %P 121--129 %U https://proceedings.mlr.press/v102/combalia19a.html %V 102 %X Specialists have used confocal microscopy in the ex-vivo modality to identify Basal Cell Carcinoma tumors with an overall sensitivity of 96.6% and specificity of 89.2% {{Chung et al.}} ({2004}). However, this technology hasn’t established yet in the standard clinical practice because most pathologists lack the knowledge to interpret its output. In this paper we propose a combination of deep learning and computer vision techniques to digitally stain confocal microscopy images into H&E-like slides, enabling pathologists to interpret these images without specific training. We use a fully convolutional neural network with a multiplicative residual connection to denoise the confocal microscopy images, and then stain them using a Cycle Consistency Generative Adversarial Network.
APA
Combalia, M., Pérez-Anker, J., García-Herrera, A., Alos, L., Vilaplana, V., Marqués, F., Puig, S. & Malvehy, J.. (2019). Digitally Stained Confocal Microscopy through Deep Learning. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 102:121-129 Available from https://proceedings.mlr.press/v102/combalia19a.html.

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