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Your search keyword '"Tsai, Chih-Chien"' showing total 182 results
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182 results on '"Tsai, Chih-Chien"'

1. A worldwide study of white matter microstructural alterations in people living with Parkinson’s disease

Author

Owens-Walton, Conor, Nir, Talia M., Al-Bachari, Sarah, Ambrogi, Sonia, Anderson, Tim J., Aventurato, Ítalo Karmann, Cendes, Fernando, Chen, Yao-Liang, Ciullo, Valentina, Cook, Phil, Dalrymple-Alford, John C., Dirkx, Michiel F., Druzgal, Jason, Emsley, Hedley C. A., Guimarães, Rachel, Haroon, Hamied A., Helmich, Rick C., Hu, Michele T., Johansson, Martin E., Kim, Ho Bin, Klein, Johannes C., Laansma, Max, Lawrence, Katherine E., Lochner, Christine, Mackay, Clare, McMillan, Corey T., Melzer, Tracy R., Nabulsi, Leila, Newman, Ben, Opriessnig, Peter, Parkes, Laura M., Pellicano, Clelia, Piras, Fabrizio, Piras, Federica, Pirpamer, Lukas, Pitcher, Toni L., Poston, Kathleen L., Roos, Annerine, Silva, Lucas Scárdua, Schmidt, Reinhold, Schwingenschuh, Petra, Shahid-Besanti, Marian, Spalletta, Gianfranco, Stein, Dan J., Thomopoulos, Sophia I., Tosun, Duygu, Tsai, Chih-Chien, van den Heuvel, Odile A., van Heese, Eva, Vecchio, Daniela, Villalón-Reina, Julio E., Vriend, Chris, Wang, Jiun-Jie, Wu, Yih-Ru, Yasuda, Clarissa Lin, Thompson, Paul M., Jahanshad, Neda, and van der Werf, Ysbrand

Published
2024
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2. Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis

Author

Dhinagar, Nikhil J., Singh, Amit, Ozarkar, Saket, Buwa, Ketaki, Thomopoulos, Sophia I., Owens-Walton, Conor, Laltoo, Emily, Chen, Yao-Liang, Cook, Philip, McMillan, Corey, Tsai, Chih-Chien, Wang, J-J, Wu, Yih-Ru, and Thompson, Paul M.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning involves pre-training deep learning models on a large corpus of data and minimally fine-tuning them for adaptation to specific tasks. Even so, for 3D medical imaging tasks, we do not know if it is best to pre-train models on natural images, medical images, or even synthetically generated MRI scans or video data. To evaluate these alternatives, here we benchmarked vision transformers (ViTs) and convolutional neural networks (CNNs), initialized with varied upstream pre-training approaches. These methods were then adapted to three unique downstream neuroimaging tasks with a range of difficulty: Alzheimer's disease (AD) and Parkinson's disease (PD) classification, "brain age" prediction. Experimental tests led to the following key observations: 1. Pre-training improved performance across all tasks including a boost of 7.4% for AD classification and 4.6% for PD classification for the ViT and 19.1% for PD classification and reduction in brain age prediction error by 1.26 years for CNNs, 2. Pre-training on large-scale video or synthetic MRI data boosted performance of ViTs, 3. CNNs were robust in limited-data settings, and in-domain pretraining enhanced their performances, 4. Pre-training improved generalization to out-of-distribution datasets and sites. Overall, we benchmarked different vision architectures, revealing the value of pre-training them with emerging datasets for model initialization. The resulting pre-trained models can be adapted to a range of downstream neuroimaging tasks, even when training data for the target task is limited.

Published
2023

3. Curriculum Based Multi-Task Learning for Parkinson's Disease Detection

Author

Dhinagar, Nikhil J., Owens-Walton, Conor, Laltoo, Emily, Boyle, Christina P., Chen, Yao-Liang, Cook, Philip, McMillan, Corey, Tsai, Chih-Chien, Wang, J-J, Wu, Yih-Ru, van der Werf, Ysbrand, and Thompson, Paul M.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here we leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN). Typically, deep learning networks are trained by randomly selecting samples in each mini-batch. By contrast, curriculum learning is a training strategy that aims to boost classifier performance by starting with examples that are easier to classify. Here we define a curriculum to progressively increase the difficulty of the training data corresponding to the Hoehn and Yahr (H&Y) staging system for PD (total N=1,012; 653 PD patients, 359 controls; age range: 20.0-84.9 years). Even with our multi-task setting using pre-trained CNNs and transfer learning, PD classification based on T1-weighted (T1-w) MRI was challenging (ROC AUC: 0.59-0.65), but curriculum training boosted performance (by 3.9%) compared to our baseline model. Future work with multimodal imaging may further boost performance., Comment: Accepted for publication at the 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

Published
2023

12. Deep Learning-Based Regional Sub-models Integration for Parkinson’s Disease Diagnosis Using Diffusion Tensor Imaging

Author

Zhao, Hengling, Tsai, Chih-Chien, Zhu, Ce, Zhou, Mingyi, Wang, Jiun-Jie, Liu, Yipeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Peng, Yuxin, editor, Hu, Shi-Min, editor, Gabbouj, Moncef, editor, Zhou, Kun, editor, Elad, Michael, editor, and Xu, Kun, editor

Published
2021
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13. Video and synthetic MRI pre-training of 3D vision architectures for neuroimage analysis

Author

Dhinagar, Nikhil J., primary, Singh, Amit, additional, Ozarkar, Saket S., additional, Buwa, Ketaki U., additional, Thomopoulos, Sophia, additional, Owens-Walton, Conor, additional, Laltoo, Emily, additional, Chen, Yao-Liang, additional, Cook, Philip A., additional, McMillan, Corey, additional, Tsai, Chih-Chien, additional, Wang, J-J, additional, Wu, Yih-Ru, additional, and Thompson, Paul M., additional

Published
2024
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14. A Worldwide Study of White Matter Microstructural Alterations in People Living with Parkinson’s Disease

Author

Owens-Walton, Conor, primary, Nir, Talia M., additional, Al-Bachari, Sarah, additional, Ambrogi, Sonia, additional, Anderson, Tim J., additional, Aventurato, Ítalo Karmann, additional, Cendes, Fernando, additional, Chen, Yao-Liang, additional, Ciullo, Valentina, additional, Cook, Phil, additional, Dalrymple-Alford, John C., additional, Dirkx, Michiel F., additional, Druzgal, Jason, additional, Emsley, Hedley C. A., additional, Guimarães, Rachel, additional, Haroon, Hamied A., additional, Helmich, Rick C., additional, Hu, Michele T., additional, Johansson, Martin E., additional, Kim, Ho Bin, additional, Klein, Johannes C., additional, Laansma, Max, additional, Lawrence, Katherine E., additional, Lochner, Christine, additional, Mackay, Clare, additional, McMillan, Corey, additional, Melzer, Tracy R., additional, Nabulsi, Leila, additional, Newman, Ben, additional, Opriessnig, Peter, additional, Parkes, Laura M., additional, Pellicano, Clelia, additional, Piras, Fabrizio, additional, Piras, Federica, additional, Pirpamer, Lukas, additional, Pitcher, Toni L., additional, Poston, Kathleen L., additional, Roos, Annerine, additional, Silva, Lucas Scárdua, additional, Schmidt, Reinhold, additional, Schwingenschuh, Petra, additional, Shahid, Marian, additional, Spalletta, Gianfranco, additional, Stein, Dan J., additional, Thomopoulos, Sophia I., additional, Tosun, Duygu, additional, Tsai, Chih-Chien, additional, van den Heuvel, Odile A., additional, van Heese, Eva, additional, Vecchio, Daniela, additional, Villalón-Reina, Julio E., additional, Vriend, Chris, additional, Wang, Jiun-Jie, additional, Wu, Yih-Ru, additional, Yasuda, Clarissa Lin, additional, Thompson, Paul M., additional, Jahanshad, Neda, additional, and van der Werf, Ysbrand, additional

Published
2024
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17. Comparison of Anatomical and Diffusion MRI for detecting Parkinson’s Disease using Deep Convolutional Neural Network

Author

Chattopadhyay, Tamoghna, primary, Singh, Amit, additional, Laltoo, Emily, additional, Boyle, Christina P., additional, Owens-Walton, Conor, additional, Chen, Yao-Liang, additional, Cook, Philip, additional, McMillan, Corey, additional, Tsai, Chih-Chien, additional, Wang, J-J, additional, Wu, Yih-Ru, additional, van der Werf, Ysbrand, additional, and Thompson, Paul M., additional

Published
2023
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19. Curriculum Based Multi-Task Learning for Parkinson’s Disease Detection

Author

Dhinagar, Nikhil J., primary, Owens-Walton, Conor, additional, Laltoo, Emily, additional, Boyle, Christina P., additional, Chen, Yao-Liang, additional, Cook, Philip, additional, McMillan, Corey, additional, Tsai, Chih-Chien, additional, Wang, J-J, additional, Wu, Yih-Ru, additional, van der Werf, Ysbrand, additional, and Thompson, Paul M., additional

Published
2023
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20. Comparison of Anatomical and Diffusion MRI for detecting Parkinson’s Disease using Deep Convolutional Neural Network

Author

Chattopadhyay, Tamoghna, Singh, Amit, Laltoo, Emily, Boyle, Christina P., Owens-Walton, Conor, Chen, Yao-Liang, Cook, Philip, McMillan, Corey, Tsai, Chih-Chien, Wang, J-J, Wu, Yih-Ru, van der Werf, Ysbrand, and Thompson, Paul M.

Subjects
Article
Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer’s disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification.Clinical RelevanceThis study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson’s disease.

Published
2023
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26. T1 and T2∗ relaxation time in the parcellated myocardium of healthy Taiwanese participants: A single center study

Author

Tsai, Chih-Chien, primary, Ng, Shu-Hang, additional, Chen, Yao-Liang, additional, Juan, Yu-Hsiang, additional, Wang, Chao-Hung, additional, Lin, Gigin, additional, Chien, Chih-Wei, additional, Lin, Yu-Ching, additional, Lin, Yu-Chun, additional, Huang, Yu-Chieh, additional, Huang, Pei-Ching, additional, and Wang, Jiun-Jie, additional

Published
2021
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46. Overexpression of hTERT increases stem-like properties and decreases spontaneous differentiation in human mesenchymal stem cell lines

Author

Wang Hsei-Wei, Lee Yi-Ting, Liu Hwa-Chung, Chen Chun-Li, Tsai Chih-Chien, Hou Lein-Tuan, and Hung Shih-Chieh

Subjects
Medicine
Abstract

Abstract To overcome loss of stem-like properties and spontaneous differentiation those hinder the expansion and application of human mesenchymal stem cells (hMSCs), we have clonally isolated permanent and stable human MSC lines by ectopic overexpression of primary cell cultures of hMSCs with HPV 16 E6E7 and human telomerase reverse transcriptase (hTERT) genes. These cells were found to have a differentiation potential far beyond the ordinary hMSCs. They expressed trophoectoderm and germline specific markers upon differentiation with BMP4 and retinoic acid, respectively. Furthermore, they displayed higher osteogenic and neural differentiation efficiency than primary hMSCs or hMSCs expressed HPV16 E6E7 alone with a decrease in methylation level as proven by a global CpG island methylation profile analysis. Notably, the demethylated CpG islands were highly associated with development and differentiation associated genes. Principal component analysis further pointed out the expression profile of the cells converged toward embryonic stem cells. These data demonstrate these cells not only are a useful tool for the studies of cell differentiation both for the mesenchymal and neurogenic lineages, but also provide a valuable source of cells for cell therapy studies in animal models of skeletal and neurological disorders.

Published
2010
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47. Fibronectin and laminin promote differentiation of human mesenchymal stem cells into insulin producing cells through activating Akt and ERK

Author

Chiou Shih-Hwa, Chen Ling-Lan, Tsai Chih-Chien, Lin Hsiao-Yun, Wang Yng-Jiin, and Hung Shih-Chieh

Subjects
Medicine
Abstract

Abstract Background Islet transplantation provides a promising cure for Type 1 diabetes; however it is limited by a shortage of pancreas donors. Bone marrow-derived multipotent mesenchymal stem cells (MSCs) offer renewable cells for generating insulin-producing cells (IPCs). Methods We used a four-stage differentiation protocol, containing neuronal differentiation and IPC-conversion stages, and combined with pellet suspension culture to induce IPC differentiation. Results Here, we report adding extracellular matrix proteins (ECM) such as fibronectin (FN) or laminin (LAM) enhances pancreatic differentiation with increases in insulin and Glut2 gene expressions, proinsulin and insulin protein levels, and insulin release in response to elevated glucose concentration. Adding FN or LAM induced activation of Akt and ERK. Blocking Akt or ERK by adding LY294002 (PI3K specific inhibitor), PD98059 (MEK specific inhibitor) or knocking down Akt or ERK failed to abrogate FN or LAM-induced enhancement of IPC differentiation. Only blocking both of Akt and ERK or knocking down Akt and ERK inhibited the enhancement of IPC differentiation by adding ECM. Conclusions These data prove IPC differentiation by MSCs can be modulated by adding ECM, and these stimulatory effects were mediated through activation of Akt and ERK pathways.

Published
2010
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48. Mechanical and Electrical Properties of MWNT/Phenolic Composites under Moisture-Temperature Effects

Author

Kuo Shu Wang, Tsai Chih Chien, and Ming-Chuen Yip

Subjects
Materials science, Moisture, Scanning electron microscope, Mechanical Engineering, Temperature cycling, Carbon nanotube, law.invention, Thermal conductivity, Mechanics of Materials, law, Electrical resistivity and conductivity, Ultimate tensile strength, General Materials Science, Chemical stability, Composite material
Published
2007
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