Ha, Junhyoung Kim, Soonkyum Baik, YaeJun Lee, Dohee Lee, Woosub Suh, SeungBeum 2024-01-19T09:09:53Z 2024-01-19T09:09:53Z 2022-03-07 2020-07 1557-170X https://pubs.kist.re.kr/handle/201004/113596 Biological experiments for developing efficient cancer therapeutics require significant resources of time and costs particularly in acquiring biological image data. Thanks to recent advances in AI technologies, there have been active researches in generating realistic images by adapting artificial neural networks. Along the same lines, this paper proposes a learning-based method to generate images inheriting biological characteristics. Through a statistical comparison of tumor penetration metrics between generated images and real images, we have shown that forged micrograph images contain vital characteristics to analyze tumor penetration performance of infecting agents, which opens up the promising possibilities for utilizing proposed methods for generating clinically meaningful image data. English IEEE Artificial Neural Network enabling Clinically Meaningful Biological Image Data Generation Conference 1 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), pp.2404 - 2407 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) 2404 2407 US Montreal, CANADA 2020-07-20 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 000621592202178 2-s2.0-85091033757