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: | WONJAE LEE |
| Institution: | University of Missouri |
| Department: | Industrial Engineering |
| Country: | |
| Proposed Analysis: | . Proposed Analysis: Active learning with image data . Traditional supervised learning algorithms use whatever labeled data is provided to induce a model. By contrast, active learning gives the learner a degree of control by allowing it to select which instances are labeled and added to the training set. In this sense, active learning is well-suited to many problems in image data, where unlabeled data may be abundant but annotation is slow and expensive. Active learning helps us reduce costs as well as efforts to collect “labeled” data for data analysis. I will develop an active learning algorithm based on reinforcement learning to learn the policy on how to take actions that make a decision on which image should be labeled to improve prediction accuracy with less the number of labeled image data. |
| Additional Investigators |
