HIGHLIGHTS
- who: Multi-Domain Endoscopic Images et al. from the (UNIVERSITY) have published the Article: Semi-supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images, in the Journal: (JOURNAL)
- what: The authors propose a semisurprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation and a multi-input student network. Significance: This study shows the potential of using semi-supervised GAN-based bladder tissue classification when annotations are limited in data. Most of the . . .
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