Deep Learning of Phase-Contrast Images of Cancer Stem Cells Using a Selected Dataset of High Accuracy Value Using Conditional Generative Adversarial Networks

• Published in: International journal of molecular sciences Vol. 24; no. 6; p. 5323
• Main Authors: Zhang, Zaijun, Ishihata, Hiroaki, Maruyama, Ryuto, Kasai, Tomonari, Kameda, Hiroyuki, Sugiyama, Tomoyasu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.03.2023; MDPI
• Subjects: Accuracy; Artificial Intelligence; Cancer; cancer stem cell; Cell culture; cell morphology; CGAN; Classification; CNN; Datasets; Deep Learning; Humans; Image contrast; Image Processing, Computer-Assisted - methods; Medical imaging; Morphology; Neoplasms - diagnostic imaging; Neoplastic Stem Cells; Neural networks; Neural Networks, Computer; segmentation; Stem cells; Technology application; Translation; Tumors; Workflow; Japan; segmentation; CNN; cell morphology; cancer stem cell; CGAN; artificial intelligence
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms24065323

Artificial intelligence (AI) technology for image recognition has the potential to identify cancer stem cells (CSCs) in cultures and tissues. CSCs play an important role in the development and relapse of tumors. Although the characteristics of CSCs have been extensively studied, their morphological features remain elusive. The attempt to obtain an AI model identifying CSCs in culture showed the importance of images from spatially and temporally grown cultures of CSCs for deep learning to improve accuracy, but was insufficient. This study aimed to identify a process that is significantly efficient in increasing the accuracy values of the AI model output for predicting CSCs from phase-contrast images. An AI model of conditional generative adversarial network (CGAN) image translation for CSC identification predicted CSCs with various accuracy levels, and convolutional neural network classification of CSC phase-contrast images showed variation in the images. The accuracy of the AI model of CGAN image translation was increased by the AI model built by deep learning of selected CSC images with high accuracy previously calculated by another AI model. The workflow of building an AI model based on CGAN image translation could be useful for the AI prediction of CSCs.