2019-05-24-combalia19a.md

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Contributed Papers	Digitally Stained Confocal Microscopy through Deep Learning	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.	inproceedings	Proceedings of Machine Learning Research	combalia19a	0	Digitally Stained Confocal Microscopy through Deep Learning	121	129	121-129	121	false	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	given family Marc Combalia given family Javiera Pérez-Anker given family Adriana García-Herrera given family Llúcia Alos given family Verónica Vilaplana given family Ferran Marqués given family Susana Puig given family Josep Malvehy	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/combalia19a/combalia19a.pdf