Better Generalization with Less Human Annotation Using Meta-Learning and Self-Supervised Learning for Image Analysis
| dc.contributor.advisor | Nguyen, Hien Van | |
| dc.contributor.committeeMember | Chen, Jiefu | |
| dc.contributor.committeeMember | Han, Zhu | |
| dc.contributor.committeeMember | Wu, Xuqing | |
| dc.contributor.committeeMember | Wong, Stephen T. | |
| dc.creator | Yuan, Pengyu | |
| dc.creator.orcid | 0000-0002-9589-411X | |
| dc.date.accessioned | 2023-05-28T17:21:51Z | |
| dc.date.created | August 2022 | |
| dc.date.issued | 2022-08-09 | |
| dc.date.updated | 2023-05-28T17:21:52Z | |
| dc.description.abstract | Deep neural networks require a large amount of annotated training data to generalize well. Unfortunately, such large training data is difficult to obtain in medical or geophysical domains due to many reasons, including the high annotation cost, privacy concerns, and physical constraints. To address the data efficiency issue in training the deep learning model, we proposed the solutions in two directions: meta-learning and self-supervised learning. Meta-learning tries to generate a robust model that can learn to quickly adapt to new tasks with minimal labeled samples. It is also called “learning to learn”, which acquires fast adaptation capability over a collection of related tasks and uses it to improve its future learning performance. Self-supervised learning, on the other hand, tries to leverage all useful information from the unlabeled training data itself. It can improve feature representation by solving a pretext task, or directly solving the unsupervised target task which shares the same task structure as the self-supervised task. In the first part of this work, we introduce the meta-learning setting, develop a meta-learning framework AGILE+ to deliver the efficient rat brain cell classifier, and study the first arrival picking problem and solve the domain shift problem with less human interaction. In the second part, we discuss different self-supervised learning settings, introduce the 3D self-supervised image patch reconstruction task to significantly improve the incidental lung nodule classification accuracy for the data-hungry 3D Vision Transformer (3D-ViT) model, and propose the self-supervised learning model Blind-Trace Network (BTN) for the application in the unsupervised seismic interpolation task. | |
| dc.description.department | Electrical and Computer Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Yuan, P., Mobiny, A., Jahanipour, J., Li, X., Cicalese, P. A., Roysam, B., ... & Van Nguyen, H. (2020). Few is enough: task-augmented active meta-learning for brain cell classification. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I 23 (pp. 367-377). Springer International Publishing; and in: Yuan, Pengyu, Shirui Wang, Wenyi Hu, Xuqing Wu, Jiefu Chen, and Hien Van Nguyen. "A robust first-arrival picking workflow using convolutional and recurrent neural networks." Geophysics 85, no. 5 (2020): U109-U119; and in: Yuan, Pengyu, Shirui Wang, Wenyi Hu, Prashanth Nadukandi, German Ocampo Botero, Xuqing Wu, Hien Van Nguyen, and Jiefu Chen. "Self-Supervised Learning for Efficient Antialiasing Seismic Data Interpolation." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1-19. | |
| dc.identifier.uri | https://hdl.handle.net/10657/14318 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Meta-learning | |
| dc.subject | Self-supervised learning | |
| dc.subject | Image analysis | |
| dc.subject | Few-shot learning | |
| dc.title | Better Generalization with Less Human Annotation Using Meta-Learning and Self-Supervised Learning for Image Analysis | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2024-08-01 | |
| local.embargo.terms | 2024-08-01 | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Electrical and Computer Engineering, Department of | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |