Name
Title | ||
|---|---|---|
Artificial Neural Network Enabling Clinically Meaningful Biological Image Data Generation |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMBC44109.2020.9176621 | 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 |
DocType | Volume | ISSN |
Conference | 2020 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junhyoung Ha | 1 | 0 | 0.34 |
| Soonkyum Kim | 2 | 0 | 0.68 |
| YaeJun Baik | 3 | 0 | 0.34 |
| Dohee Lee | 4 | 0 | 1.35 |
| Woosub Lee | 5 | 127 | 15.75 |
| SeungBeum Suh | 6 | 29 | 2.58 |