Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging

no code implementations • 16 Apr 2023 • JieLin Qiu, Peide Huang, Makiya Nakashima, Jaehyun Lee, Jiacheng Zhu, Wilson Tang, Pohao Chen, Christopher Nguyen, Byung-Hak Kim, Debbie Kwon, Douglas Weber, Ding Zhao, David Chen

Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare.

Anatomy Image Retrieval +3

Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging

no code implementations • 25 May 2022 • Makiya Nakashima, Inyeop Jang, Ramesh Basnet, Mitchel Benovoy, W. H. Wilson Tang, Christopher Nguyen, Deborah Kwon, Tae Hyun Hwang, David Chen

Training deep learning models on cardiac magnetic resonance imaging (CMR) can be a challenge due to the small amount of expert generated labels and inherent complexity of data source.

Anatomy Contrastive Learning

An Ensemble Approach toward Automated Variable Selection for Network Anomaly Detection

no code implementations • 28 Oct 2019 • Makiya Nakashima, Alex Sim, Youngsoo Kim, Jonghyun Kim, Jinoh Kim

While variable selection is essential to optimize the learning complexity by prioritizing features, automating the selection process is preferred since it requires laborious efforts with intensive analysis otherwise.

Anomaly Detection Variable Selection