no code implementations • 21 Dec 2023 • Wenhui Cui, Haleh Akrami, Ganning Zhao, Anand A. Joshi, Richard M. Leahy
To explore the generalizability of the foundation model in downstream applications, we then apply the model to an unseen TBI dataset for prediction of PTE using zero-shot learning.
1 code implementation • 7 Nov 2023 • Wenhui Cui, Woojae Jeong, Philipp Thölke, Takfarinas Medani, Karim Jerbi, Anand A. Joshi, Richard M. Leahy
To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model.
no code implementations • 8 Oct 2023 • Ganning Zhao, Wenhui Cui, Suya You, C. -C. Jay Kuo
Unsupervised image-to-image (I2I) translation learns cross-domain image mapping that transfers input from the source domain to output in the target domain while preserving its semantics.
no code implementations • 16 Dec 2022 • Wenhui Cui, Haleh Akrami, Anand A. Joshi, Richard M. Leahy
Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data.
1 code implementation • 8 Aug 2022 • Haleh Akrami, Wenhui Cui, Anand A Joshi, Richard M. Leahy
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications.
1 code implementation • 3 Mar 2022 • Wenhui Cui, Haleh Akrami, Anand A. Joshi, Richard M. Leahy
The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural networks.
1 code implementation • 4 Mar 2019 • Wenhui Cui, Yanlin Liu, Yuxing Li, Menghao Guo, Yiming Li, Xiuli Li, Tianle Wang, Xiangzhu Zeng, Chuyang Ye
Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available.