Digitally stained confocal microscopy through deep learning
Document typeConference report
Rights accessOpen Access
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
CitationCombalia Escudero, M. [et al.]. Digitally stained confocal microscopy through deep learning. A: International Conference on Medical Imaging with Deep Learning. "International Conference on Medical Imaging with Deep Learning: 8-10 July 2019, London, United Kingdom: proceedings of Machine Learning Research". Brookline, MA: Microtome Publishing, 2019, p. 121-129.
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