Dr Euijoon Ahn ~ Lecturer, Information Technology

I am a Lecturer at the College of Science and 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. 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 have produced top-tier publications in the area of computer vision and medical image computing, including papers in IEEE T-MI, T-BME, JBHI, MedIA, PR, CVPR, AAAI and MICCAI. I am a regular reviewer for IEEE T-PAMI, T-MI, Nature Communications, CVPR, MICCAI and ISBI. 

I am always looking for highly-motivated undergraduate (Honours) and postgraduate students (MPhil and PhD). Please send me your CV and transcripts. 

Available / Current Projects

Unsupervised / Self-supervised learning for Image Classification and Object Recognition

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods have made important breakthroughs in natural scene analysis, visual object detection / classification and medical image analysis. These deep learning approaches, however, require large labelled training data (e.g., ImageNet archive with over 1 million images) and the labelling must be done manually which is costly, slow, and can be subjective / prone to errors if specialised skills are required. This project aims to study various techniques of unsupervised / self-supervised learning representation learning, and to develop new approaches for various applications (e.g., computer-aided diagnosis) in the medical imaging domain (X-ray, Ultrasound, microscopy, MRI, CT, PET-CT).

A Deep Learning Framework for Hidden Pattern Discovery

This project aims to develop a machine learning framework, which is not dependent on labelled data, to discover hidden patterns within data. These ‘hidden patterns’ represent meaningful new knowledge. Existing machine learning methods learn data features that are directly associated with labels and require large amounts of labelled data. This project expects to develop an unsupervised deep learning framework that will learn features that are independent of labels from large unlabelled image data archives. The expected outcome is a novel image analytic framework that can be applied to a range of scientific applications.

Telehealth – AI-enabled Remote Patient Monitoring

Remote patient monitoring (RPM) has seen tremendous uptake in usage due to the outbreak of COVID-19 and transition to remote patient care, and with it, a rapid rise in the development of new technologies. In an RPM setting, accurate remote measurement of vital signs such as heart rate (HR), blood pressure (BP) and temperature are essential in accurate diagnosis and screening of diseases. Current measurement methods, however, are constrained by wearable sensors or inflatable cuff-based devices that could be inconvenient, uncomfortable and are subject to variability. Development of a system that can measure such vital signs conveniently and comfortably, in a contactless and COVID-safe manner, is therefore of important requirement for use in RPM. This project aims to develop AI-enabled deep learning techniques that estimate vital signs using facial videos. 

Teaching

• CP1401: Problem Solving and Programming I (Level 1; CNS)
• CP1404: Programming II (Level 1; CNS & TSV)
• CP1407: Introductory Machine Learning and Data Science (Level 1; CNS & TSV)
• CP1409: Operating Systems and Shell Scripting (Level 1; CNS & TSV)
• CP2414: Network Security (Level 2; CNS)
• CP5632: Programming II (Level 5; CNS & TSV)
• CP5639: Problem Solving and Programming I (Level 5; CNS)

Interests

Research

• My research focus is on the development of Machine Learning and Deep Learning, Computer Vision and, more specifically, unsupervised and self-supervised deep learning models for biomedical image analysis, for improving image segmentation, retrieval, quantification and classification without relying on labelled data.
• I also work at the coalface of translational health technology researches, e.g., health data analytics and telehealth.
• Keywords: Deep Learning, Machine Learning, Data Science, Medical Image Analysis, Health Informatics, Telehealth

Experience

• 2022 to present - Lecturer, James Cook University (Cairns)
• 2020 to 2022 - Postdoctoral Research Fellow, The University of Sydney (Sydney)
• 2016 to 2022 - Research Associate, Nepean Blue Mountains Local Health District (Sydney)
• 2016 to 2020 - Research and Teaching Assistant, The University of Sydney (Sydney)
• 2010 to 2016 - Consultant, ECNESOFT (Sydney)

Research Disciplines

• Computer vision and multimedia computation (4603)
• Machine learning (4611)

Socio-Economic Objectives

• Information systems, technologies and services (2204)
• information and communication services (2299)
• health (2099)

Awards

• 2022 to 2023 - Fusion of wearable and environmental sensors for remote monitoring of health and wellbeing in elderly populations ($50,000) , The Northern Australian Regional Digital Health Collaborative
• 2022 to 2023 - Patient-centered Digital Platform for Early-stage Chronic Kidney Disease ($50,000) , The Northern Australian Regional Digital Health Collaborative
• 2019 to 2020 - Telehealth Technology Innovation Award ($50,000), “DeepVitals for Remote Patient Monitoring – A Non-contact Image-based Blood Pressure via Deep Photoplethysmography Network”, Nepean Blue Mountains Local Health District (Nepean Hospital), NSW, 2019.
• 2019 - Best Oral Presentation Award ($500), “A Self-Supervised Deep Learning Framework for Plane Identification in Fetal Ultrasound Images”, Annual Nepean Research Day, The University of Sydney Nepean Clinical School, 2019.
• 2019 - 2019 School of Computer Science Research Students Excellence Prize Award ($5000), The University of Sydney, 2019.
• 2018 to 2019 - Telehealth Technology Innovation Award ($10,000), “An Analysis and Visualisation of Emergency Department Data using Machine Learning Algorithms”, Nepean Blue Mountains Local Health District (Nepean Hospital), NSW, 2018.
• 2016 to 2019 - Scholarship, Australian Government Research Training Program Scholarship, 2016-2019.
• 2016 to 2019 - Scholarship ($17,500), The University of Sydney Merit Awards (Top ranked PhD Candidates).

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles

• Jiang C, Yuan Y, Gu B, Ahn E, Kim J, Feng D, Huang Q and Song S (in press) Preoperative prediction of microvascular invasion and perineural invasion in pancreatic ductal adenocarcinoma with 18F-FDG PET/CT radiomics analysis. Clinical Radiology,
• Kong Z, Ouyang H, Cao Y, Huang T, Ahn E, Zhang M and Liu H (2023) Automated periodontitis bone loss diagnosis in panoramic radiographs using a bespoke two-stage detector. Computers in Biology and Medicine, 152.
• Wang W, Ahn E, Feng D and Kim J (2023) A Review of Predictive and Contrastive Self-supervised Learning for Medical Images. Machine Intelligence Research, 20 (4). pp. 483-512
• Ahn E, Liu N, Parekh T, Patel R, Baldacchino T, Mullavey T, Robinson A and Kim J (2021) A mobile app and dashboard for early detection of infectious disease outbreaks: development study. JMIR Public Health and Surveillance, 7 (3).
• Ahn E, Kumar A, Fulham M, Feng D and Kim J (2020) Unsupervised domain adaptation to classify medical images using zero-bias convolutional auto-encoders and context-based feature augmentation. IEEE Transactions on Medical Imaging, 39 (7). pp. 2385-2394
• Ahn E, Kumar A, Fulham M, Feng D and Kim J (2019) Convolutional sparse kernel network for unsupervised medical image analysis. Medical Image Analysis, 56. pp. 140-151
• Bi L, Kim J, Ahn E, Kumar A, Feng D and Fulham M (2019) Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recognition, 85. pp. 78-89
• Ahn E, Kim J, Rahman K, Baldacchino T and Baird C (2018) Development of a risk predictive scoring system to identify patients at risk of representation to emergency department: a retrospective population-based analysis in Australia. BMJ Open, 8.

Conference Papers

• Wang H, Ahn E and Kim J (2022) Self-Supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss. Proceedings of the 36th AAAI Conference on Artificial Intelligence. In: AAAI-22: 36th AAAI Conference on Artificial Intelligence, 22 February - 1 March 2022, Virtual
• Ahn E, Feng D and Kim J (2021) A spatial guided self-supervised clustering network for medical image segmentation. Lecture Notes in Computer Science. In: MICCAI 2021: International Conference on Medical Image Computing and Computer-Assisted Intervention, 27 September - 1 October 2021, Strasbourg, France
• Guo Y, Bi L, Ahn E, Feng D, Wang Q and Kim J (2020) A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. In: CVPR 2020: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13-19 June 2020, Seattle, WA, USA
• Ahn E, Kumar A, Feng D, Fulham M and Kim J (2019) Unsupervised Deep Transfer Feature Learning for Medical Image Classification. Proceedings of the IEEE 16th International Symposium on Biomedical Imaging. In: ISB I2019: IEEE 16th International Symposium on Biomedical Imaging, 8-11 April 2019, Venice, Italy

More

ResearchOnline@JCU stores 18+ research outputs authored by Dr Euijoon Ahn from 2016 onwards.

Advisory Accreditation: I can be on your Advisory Panel as a Primary or Secondary Advisor.

These Higher Degree Research projects are either current or by students who have completed their studies within the past 5 years at JCU. Linked titles show theses available within ResearchOnline@JCU.

Current

• Lightweight Self-supervised learning for Image Classification and Object Recognition (PhD , Primary Advisor)
• Species Classification using deep learning-Based signal processing techniques in natural soundscapes (PhD , Secondary Advisor)