There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Jack Pollard |
| Institution: | University of Bath |
| Department: | Computer Science |
| Country: | |
| Proposed Analysis: | For my Masters dissertation I propose to investigate whether the performance of a deep learning model to predict AD/MCI can be improved with the use of a Generative Adversarial Network (GAN). I plan to build on the work of Ding et al. (2019) by using a GAN to generate additional test cases to augment the training data set. This approach has been shown to be effective in disciplines such as liver lesion classification (Frid-Adar et al., 2018) and tomato leaf disease identification (Wu et al., 2020). If successful, this would demonstrate the feasibility of such a technique to improve the prediction rate of AD/MCI. Additionally, it could have significant benefits for the computational medical identification of rarer conditions, where less training data is available. Ding, Y., Sohn, J.H., Kawczynski, M.G., Trivedi, H., Harnish, R., Jenkins, N.W., Lituiev, D., Copeland, T.P., Aboian, M.S., Mari Aparici, C. and Behr, S.C., 2019. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology, 290(2), pp.456-464. Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J. and Greenspan, H., 2018. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 321, pp.321-331. Wu, Q., Chen, Y. and Meng, J., 2020. DCGAN-based data augmentation for tomato leaf disease identification. IEEE Access, 8, pp.98716-98728. |
| Additional Investigators |
