Smart Health and Medical Imaging

CVIU Lab, Department of Computer Science and Computer Engineering
University of Arkansas
https://uark-cviu.github.io/
Email: khoaluu@uark.edu

Smart Health and Medical Imaging

Introduction

Smart health and medical imaging are two rapidly evolving fields in healthcare that are transforming the way we diagnose and treat diseases. Smart health is a concept that involves the integration of technology, such as wearables and health monitoring devices, with healthcare delivery systems to provide more personalized and efficient care to patients. On the other hand, medical imaging involves the use of advanced technologies to produce images of the human body, which are then used by healthcare professionals to diagnose and treat diseases. The combination of smart health and medical imaging has the potential to revolutionize healthcare by providing more accurate and timely diagnoses, reducing healthcare costs, and improving patient outcomes. With the advent of machine learning and artificial intelligence, medical imaging is becoming more sophisticated, enabling healthcare professionals to identify and diagnose diseases more accurately and efficiently. This project page aims to explore the latest developments in smart health and medical imaging, as well as their applications in healthcare. We will examine the latest technologies and trends in the field, and their potential impact on patient care. Additionally, we will explore the ethical and social implications of these technologies, and how they can be used to address some of the most pressing healthcare challenges of our time.




Our Solution

Our CVIU Lab focuses on four major technologies for smart health:

1. Facial Expression Techonolgy. We aim to build a robust model to accuratelty identify the micro facial expression.

2. Early Autism Detection We aim to build the AI model to early recognize autism of children.

3. Human Behavior Analysis. We aim to build the AI model that can monitor the human behavior.

4. Neural Cell Analysis. We aim to build the AI model that can monitor the development of neural cells.

Our Team

Dr. Khoa Luu Dr. Han-Seok Seo Dr. Min Zou Xuan Bac Nguyen Thanh Dat Truong Naga Venkata Sai Raviteja Chappa Manuel Serna-Aguilera
Assistant Professor Associate Professor Distinguished Professor Ph.D. Candidate Ph.D. Student Ph.D. Candidate Ph.D. Student
khoaluu@uark.edu hanseok@uark.edu mzou@uark.edu xnguyen@uark.edu tt032@uark.edu nchappa@uark.edu mserna@uark.edu

Publication

[1] Xuan-Bac Nguyen, Chi Nhan Duong, Xin Li, Susan Gauch, Han-Seok Seo, and Khoa Luu (2023). Micron-BERT: BERT-based Facial Micro-Expression Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

[2] Manuel Serna-Aguilera, Khoa Luu, Nathaniel Harris, and Min Zou (2023). Neural Cell Video Synthesis via Optical-Flow Diffusion. arXiv, 2023.

[3] Naga VS Chappa, Pha Nguyen, Alexander H. Nelson, Han-Seok Seo, Xin Li, Page Daniel Dobbs, and Khoa Luu (2023). Group Activity Recognition using Self-supervised Approach of Spatiotemporal Transformers. arXiv, 2023.

[4] Thanh-Dat Truong, Quoc-Huy Bui, Chi Nhan Duong, Han-Seok Seo, Son Lam Phung, Xin Li, and Khoa Luu (2022). DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

[5] Ngan Le, James Sorensen, Toan Duc Bui, Arabinda Choudhary, Khoa Luu, and Hien Nguyen (2021). Pairflow: Enhancing portable chest x-ray by flow-based deformation for covid-19 diagnosing. In IEEE International Conference on Image Processing (ICIP), 2021.

[6] Ngan Le, Toan Bui, Viet-Khoa Vo-Ho, Kashu Yamazaki, and Khoa Luu (2021). Narrow band active contour attention model for medical segmentation. Diagnostics, 2021.

[7] Ibsa K. Jalata, Thanh-Dat Truong, Jessica L. Allen, Han-Seok Seo, and Khoa Luu (2021). Movement analysis for neurological and musculoskeletal disorders using graph convolutional neural network. Future Internet, 2021.

Sponsors