Euijoon AhnLecturer (Assistant Professor) Discipline of Information Technology, College of Science and Engineering |
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Short BiographyI am a Lecturer at the College of Sciene of Engineering, James Cook University.
Prior to this, I was a postdoctoral research fellow at the Biomedical Multimedia Information Technology (BMIT) group, School of Computer Science, The University of Sydney.
I obtained my PhD degree in Computer Science (medical image analysis) from The University of Sydney in 2020 under the supervision of Dr. Ashnil Kumar, Prof. David Feng, and Prof. Jinman Kim.
I received B. IT degree from The University of Newcastle, Australia, 2009 and M. IT (2014) and MPhil (2016) degree from The University of Sydney. I was an IT Consultant at the ECNESOFT, in Australia between 2010 to 2016. I was born in , raised in (finished up to high school) and educated in (Uni). |
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Research AreasDeep Learning, Machine Learning, Data Science, Medical Image Analysis, Health Informatics, Telehealth My research focus is on the development of unsupervised deep learning models for medical image analysis, for improving image segmentation, retrieval, quantification and classification without relying on labelled data. I am always looking for highly-motivated undergraduate (Honours) and postgraduate students (MPhil and PhD). Please send me your CV and transcripts. |
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Selected PublicationsT. Napier, E. Ahn, S. Allen-Ankins and I. Lee, "An Optimised Grid Search Based Framework for Robust Large-Scale Natural Soundscape Classification", 36th Australasian Joint Conference on Artificial Intelligence, 2023. [Accepted to appear] S. Zhou, E. Ahn, H. Wang, A. Quinton, N. Kennedy, P. Sridar, R. Nanan and J. Kim, "Improving Automatic Fetal Biometry Measurement with Swoosh Activation Function", International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023. [paper] C. Jiang, Y. Yuan, B. Gu, E. Ahn, J. Kim, D. Feng, Q. Huang and S. Song, "Preoperative prediction of microvascular invasion and perineural invasion in pancreatic ductal adenocarcinoma with 18F-FDG PET/CT radiomics analysis", Clinical Radiology, 2023. (IF=2.6) [paper] H. Wang, E. Ahn and J. Kim, "A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images", pre-print, 2023. [paper][code] S. Zhou, P. Sridar, R. Nanan, A. Quinton, E. Ahn and J. Kim, "A Deep-Learning Enabled Automatic Fetal Thalamus Diameter Measurement Algorithm ", IEEE Engineering in Medicine and Biology Society (EMBC), 2023. [Accepted to appear] Y. Yuan, E. Ahn, D. Feng, M. Khadra and J. Kim, "SSPT-bpMRI: A Self-supervised Pre-training Scheme for Improving Prostate Cancer Detection and Diagnosis in Bi-parametric MRI ", IEEE Engineering in Medicine and Biology Society (EMBC), 2023. [Accepted to appear] Z. Kong, H. Ouyang, Y. Cao, T. Huang, E. Ahn, M. Zhang and H. Liu, "Automated Periodontitis Bone Loss Diagnosis in Panoramic Radiographs using a Bespoke Two-stage Detector", Computers in Biology and Medicine, 2023. (IF=7.7) [paper][code and dataset] W. Wang, E. Ahn, D. Feng and J. Kim, "A Review of Predictive and Contrastive Self-supervised Learning for Medical Images", Machine Intelligence Research, 2023. [paper] H. Wang, E. Ahn and J. Kim, "SLF-RPM: Self-supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss", 36th AAAI Conference on Artificial Intelligence (AAAI-22), 2022. (Acceptance Rate: 15%) [paper][code] E. Ahn, D. Feng and J. Kim, "A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation", International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021. [paper][code][poster][slides][video] E. Ahn, N. Liu, T. Parekh, R. Patel, T. Baldacchino, T. Mullavey, A. Robinson and J. Kim, "A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study", JMIR Public Health and Surveillance, 2021. (IF=8.5) [paper] C. Baird, Y. Oytam, K. Rahman, M. Fornasari, A. Sharma, J. Kim, E. Ahn and R. Hughes, "The Checkpoint Program: Collaborative Care to Reduce the Reliance of Frequent Presenters on Emergency Department", International Journal of Integrated Care, 2021. (IF=2.913) [paper] E. Ahn, "Unsupervised Deep Feature Learning for Medical Image Analysis", The University of Sydney (PhD Thesis), 2020. [paper] Y. Guo, L. Bi, E. Ahn, D. Feng, Q. Wang and J. Kim, "A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (Acceptance Rate: 22.1%) [paper] [code] E. Ahn, A. Kumar, M. Fulham, D. Feng and J. Kim, "Unsupervised Domain Adaptation to Classify Medical Images using Zero-bias Convolutional Auto-encoders and Context-based Feature Augmentation", IEEE Transactions on Medical Imaging, 2020. (IF=10.6). [paper][code] E. Ahn, A. Kumar, D. Feng, M. Fulham and J. Kim, "Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis", Medical Image Analysis, 2019. (IF=10.9). [paper] E. Ahn, A. Kumar, D. Feng, M. Fulham and J. Kim., "Unsupervised Deep Transfer Feature Learning for Medical Image Classification", IEEE International Symposium on Biomedical Imaging (ISBI), 2019. (Oral). [paper][code] L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham and D. Feng., "Step-wise Integration of Deep Class-specific Learning for Dermoscopic Image Segmentation", Pattern Recognition, 2019. (IF=8). [paper] E. Ahn, J. Kim, K. Rahman, T. Baldachhino and C. Baird, "Development of a Risk Predictive Scoring System to Identify Patients at Risk of Representation to Emergency Department: a Retrospective Population-based Analysis in Australia", British Medical Journal Open (BMJ Open), 2018. (IF=2.9). [paper][supplementary data] E. Ahn, J. Kim, L. Bi, A. Kumar, C. Li, M. Fulham and D. Feng, "Saliency-based Lesion Segmentation via Background Detection in Dermoscopic Images", IEEE Journal of Biomedical Health Informatic, 2017. (IF=7.7). [paper] L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham and D. Feng, "Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks", IEEE Transactions on Biomedical Engineering, 2017. (IF=4.6). [paper] |
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Research Group Current PhD Students (as primary or secondary supervisor)
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Teaching
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Scientific Community Service Workshop Organisation
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Last updated date: 11 Oct 2023. |