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

Keywords: Deep learning, Neural Networks, Digital Staining, Confocal Microscopy, Speckle Noise, CycleGAN

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.

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