| Biography | |
|---|---|
 Dr. Gan Hong Seng Xi'an Jiaotong - Liverpool University, China |
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| Abstract: Knee Osteoarthritis (OA) is the most prevalent joint degenerative disease affecting the aged population. Recent reports have shown the disease has become more common among younger people. Cartilage deformation is the primary feature to analysis the progression of knee OA. Therefore, segmentation of knee cartilage plays crucial role in reliable knee OA computer-aided diagnosis pipeline. Previous attempts by using convolutional neural network (CNN) have limitation in learning the varying anatomy and thin architecture of knee cartilage. On the other hand, the properties of graph have demonstrated great potential to extract salient feature representation by investigating the relationships of pairwise connections. In this work, a graph deep learning cartilage segmentation framework has been developed. The knee image data is transformed into node representation. According to the findings, the graph deep learning segmentation model has shown an accuracy of 0.8528±0.16 and DSC of 0.8432±0.19. Future works should investigate the direct implementation of graph deep learning for 3D segmentation. | |
| Biography: Dr. Gan Hong Seng received his PhD in Biomedical Engineering from Universiti Teknologi Malaysia in 2016. Currently, he is an Associate Professor at the School of AI and Advanced Computing, Xi'an Jiaotong - Liverpool University. Dr. Gan research areas specialize in the use of artificial intelligence for medical image analysis. His research develops machine learning and computer vision algorithms for computer-aided diagnosis of Knee Osteoarthritis. in 2018, Dr. Gan was invited as a JSPS Research Fellow at Meiji University, Japan. Besides, he serves as the Adjunct Professor of SRM Institute of Engineering and Technology, India and Academic Fellow of Universitas Airlangga, Indonesia. To date, Dr. Gan has published more than 46 journal papers and won special awards in numerous international and national exhibitions. He has vast experience in supervising postgraduate students and postdoctoral researchers. | |
