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However, the standard procedure of histological examination often involves complicated tissue fixation and staining, which are time\u2010consuming and may delay the diagnosis and surgery. In this study, label\u2010free multiphoton microscopy (MPM) was used to acquire subcellular\u2010resolution images of unstained prostate tissues. Then, a deep learning architecture (U\u2010net) was introduced for epithelium segmentation of prostate tissues in MPM images. The obtained segmentation results were then merged with the original MPM images to train a classification network (AlexNet) for automated Gleason grading. The developed method achieved an overall pixel accuracy of 92.3% with a mean F1 score of 0.839 for epithelium segmentation. By merging the segmentation results with the MPM images, the accuracy of Gleason grading was improved from 72.42% to 81.13% in hold\u2010out test set. Our results suggest that MPM in combination with deep learning holds the potential to be used as a fast and powerful clinical tool for PCa diagnosis.<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/jbio201900203-gra-0001.png\" xlink:title=\"image\"\/><\/jats:p>","DOI":"10.1002\/jbio.201900203","type":"journal-article","created":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T18:30:01Z","timestamp":1573497001000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Epithelium segmentation and automated Gleason grading of prostate cancer via deep learning in label\u2010free multiphoton microscopic images"],"prefix":"10.1002","volume":"13","author":[{"given":"Qinqin","family":"Yang","sequence":"first","affiliation":[{"name":"Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of OptoElectronic Science 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