Fig 6:

DL approaches to support real-time, automated diagnostic assessment of tissues with confocal laser endomicroscopy. (a) Graphical rendering of two confocal laser endomicroscopy probes (left: Cellvizio, right: Pentax) (adapted from [109]). (b) Example CLE images obtained from four different regions of the oral cavity (adapted from [110]) (c) Fine-tuning of CNNs pre-trained using ImageNet is utilized in the majority of CLE papers reported since 2017 (adapted from [110]). (d) Super-resolution networks for probe-based CLE images incorporate novel layers to better account for the sparse, irregular structure of the images (adapted from [111]). (e) Example H&E stained histology images with corresponding CLE images. Adversarial training of GANs to transfer between these two modalities has been successful (adapted from [112]). (f) Transfer recurrent feature learning utilizes adversarially trained discriminators in conjunction with an LSTM for state-of-the-art video classification performance (adapted from [112]).