Johan Verjans

Associate Professor Johan Verjans

Associate Professor

Australian Institute for Machine Learning - Operations

Division of Research and Innovation

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

A/Prof Johan Verjans is a clinician-scientist at the Royal Adelaide Hospital with a research focus on cross-disciplinary translational research with a track record in leadership. In his role at the Australian Institute for Machine Learning (AIML), a world-renowned institute in Machine Learning, and as platform leader AI at SAHMRI, he combines experience in molecular medicine and clinical research with vast experience working with engineers for advanced imaging techniques, and computer scientists to apply machine learning to medical problems to translate research into the clinic.

He graduated in medicine from Maastricht University via an MD-PhD track after being awarded the DiPalma Fellowship to work with the renowned Professor Narula in Philadelphia and UC Irvine. During cardiology training, he was awarded a prestigious Rubicon Fellowship by the Dutch Science Foundation to complete a post-doctoral fellowship at Massachusetts General Hospital/Harvard Medical School, followed by a clinician-scientist award at the University Medical Centre Utrecht during his cardiology training. He was recruited by the University of Adelaide in 2017 and was in 2022 recipient of the University's Future Industry Making Fellowship.

Since then, he has formed the Medical Machine Learning group at AIML and used his combined clinical, biomedical and technical expertise to lead a rapidly growing group of clinicians, biomedical researchers and computer scientists with the objective of making the University of Adelaide a global leader in AI in Healthcare, using existing globally renowned AI expertise at AIML. The group has grown to over 25 members, won international technology challenges and has contracts with pharma and technology companies such as GSK, Roche, Siemens Healthineers and Medtronic. The group's efforts have caught international attention, leading to an invitation from a renowned consortium of Medical AI Institutes. A testament to their diligence, their institute was distinguished as the AI Centre in Medicine of the Year in 2022 by the AI Global Summit. His interest in AI/ML, accumulated over the past 9 years, reflects an aim to weave machine learning techniques into clinical realms, and he's collaborating with governmental entities to achieve this. He advises companies and is also a member of the global GSK AI advisory board.

His research has been published in leading cardiology journals Circulation, JACC, JAMA Cardiology, Nature Reviews Cardiology, JACC CV Imaging, Light: Science and Applications, IEEE Transaction in Medical Imaging. The most significant achievement in computer science is that his team won the global Medical VQA challenge organised by the NIH. VQA was at that time one of the most challenging areas in machine learning, whereby the computer is programmed to answer open-ended questions from medical images, which was the early version of medical chatGPT.

He has given presentations at all major cardiovascular conferences, including several Young Investigator award sessions. He was chair of the Australian Society for Molecular Imaging Conference, and a member of the Publications Committee of the Society for Cardiac MR evaluating clinical imaging guidelines and consensus statements. He was the lead author of a chapter in the first book of AI in Medicine (Springer/Nature) and contributed to several other chapters, including an international expert group to standardise machine learning methods in Medical Imaging. He is an editorial board member of the European Heart Journal Digital Health and Frontiers in Cardiology and is Associate Editor of the Netherlands Heart Journal and a member of the publication committee in a leading imaging society, the Society of Cardiac MRI.

Mentorship-wise, he provides invaluable guidance to budding researchers and students, creating a nurturing academic environment with a fantastic mix of engineers, clinicians, and computer scientists.

Research interests - Machine Learning, Translational Research, Clinical Innovation, Digital health

Pre-clinical 
-Noninvasive (Molecular) Imaging of Atherosclerosis, Myocardial infarction, Heart Failure (PET/SPECT, Optical, MRI, CT) 
-Intravascular Imaging of Atherosclerosis (OCT, Hybrid Fluorescence/OCT Catheters)

Clinical (CT, MR)
-Noninvasive imaging of atherosclerotic disease (CT, MRI)
-Noninvasive imaging of interstitial changes after myocardial infarction (SPECT/PET, MRI)
-Population Imaging

Medical Machine learning 

-Image analysis (segmentation, classification)
-Prediction
-Multimodal learning (genomics, metabolomics, imaging)

-Sensors

  • Journals
    Year Citation
    2024 Tian, Y., Pang, G., Liu, Y., Wang, C., Chen, Y., Liu, F., . . . Carneiro, G. (2024). Unsupervised Anomaly Detection in Medical Images with a Memory-Augmented Multi-level Cross-Attentional Masked Autoencoder. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14349 LNCS, 11-21.
    DOI Scopus1
    2024 Xie, Y., Zhang, J., Liu, L., Wang, H., Ye, Y., Johan, V., & Xia, Y. (2024). ReFs: A hybrid pre-training paradigm for 3D medical image segmentation. Medical Image Analysis, 91, 10 pages.
    DOI
    2024 Bedrikovetski, S., Zhang, J., Seow, W., Traeger, L., Moore, J. W., Verjans, J., . . . Sammour, T. (2024). Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer. Journal of Medical Imaging and Radiation Oncology, 68(1), 33-40.
    DOI
    2024 Saad, F. H., Farook, T. H., Ahmed, S., Zhao, Y., Liao, Z., Verjans, J. W., & Dudley, J. (2024). Facial and mandibular landmark tracking with habitual head posture estimation using linear and fiducial markers. Healthcare Technology Letters, 11(1), 21-30.
    DOI
    2023 Al-Qadami, G., Bowen, J., Van Sebille, Y., Secombe, K., Dorraki, M., Verjans, J., . . . Le, H. (2023). Baseline gut microbiota composition is associated with oral mucositis and tumour recurrence in patients with head and neck cancer: a pilot study.. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 31(1), 98.
    DOI Scopus11 WoS5 Europe PMC9
    2023 Moss, A., Daghem, M., Tzolos, E., Meah, M. N., Wang, K. -L., Bularga, A., . . . PREFFIR Investigators. (2023). Coronary Atherosclerotic Plaque Activity and Future Coronary Events. JAMA cardiology, 8(8), 755-764.
    DOI Scopus9 WoS5 Europe PMC1
    2023 Phan, V. M. H., Liao, Z., Verjans, J. W., & To, M. S. (2023). Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14229 LNCS, 56-65.
    DOI
    2023 Rana, K., Beecher, M. B., Caltabiano, C., Zhao, Y., Verjans, J., & Selva, D. (2023). Normal periocular anthropometric measurements in an Australian population. International Ophthalmology, 43(8), 2695-2701.
    DOI Scopus1 Europe PMC1
    2023 Galdran, A., Verjans, J. W., Carneiro, G., & González Ballester, M. A. (2023). Multi-Head Multi-Loss Model Calibration. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14222 LNCS, 108-117.
    DOI Scopus1
    2023 Sasanelli, F., Le, K. D. R., Tay, S. B. P., Tran, P., & Verjans, J. W. (2023). Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review. APPLIED SCIENCES-BASEL, 13(20), 17 pages.
    DOI
    2023 Galdran, A., Verjans, J., Carneiro, G., & Ballester, M. Á. G. (2023). Multi-Head Multi-Loss Model Calibration.. CoRR, abs/2303.01099.
    2023 Prijs, J., Liao, Z., To, M. -S., Verjans, J., Jutte, P. C., Stirler, V., . . . Machine Learning Consortium. (2023). Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN).. European journal of trauma and emergency surgery : official publication of the European Trauma Society, 49(2), 1057-1069.
    DOI Scopus8 WoS1 Europe PMC4
    2023 Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J. W., . . . Carneiro, G. (2023). Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis, 90, 102930-1-102930-11.
    DOI Scopus1
    2022 Li, J., Montarello, N., Hoogendoorn, A., Verjans, J., Bursill, C., Peter, K., . . . Psaltis, P. (2022). Multimodality intravascular imaging of high-risk coronary plaque. JACC: Cardiovascular Imaging, 15(1), 145-159.
    DOI Scopus35 WoS22 Europe PMC16
    2022 Dorraki, M., Muratovic, D., Fouladzadeh, A., Verjans, J. W., Allison, A., Findlay, D. M., & Abbott, D. (2022). Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures. PNAS Nexus, 1(5), pgac258-1-pgac258-11.
    DOI Scopus1
    2022 White, J. B., Trim, P. J., Salagaras, T., Long, A., Psaltis, P. J., Verjans, J. W., & Snel, M. F. (2022). Equivalent Carbon Number and Interclass Retention Time Conversion Enhance Lipid Identification in Untargeted Clinical Lipidomics.. Analytical chemistry, 94(8), 3476-3484.
    DOI Scopus13 WoS7 Europe PMC5
    2022 Liao, Z., Liao, K., Shen, H., Van Boxel, M. F., Prijs, J., Jaarsma, R. L., . . . Verjans, J. W. (2022). CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification.. IEEE J Biomed Health Inform, 26(7), 3139-3150.
    DOI Scopus5
    2022 Li, J., Thiele, S., Kirk, R. W., Quirk, B. C., Hoogendoorn, A., Chen, Y. C., . . . McLaughlin, R. A. (2022). 3D-printed micro lens-in-lens for in vivo multimodal microendoscopy. Small, 18(17), 2107032-1-2107032-8.
    DOI Scopus24 WoS13 Europe PMC5
    2022 Dorraki, M., Muratovic, D., Fouladzadeh, A., Verjans, J., Allison, A., Findlay, D., & Abbott, D. (2022). Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures.
    DOI
    2021 White, J., Trim, P., Salagaras, T., Long, A., Psaltis, P., Verjans, J., & Snel, M. (2021). Effective Carbon Number and Inter-Class Retention Time Conversion Enhances Lipid Identifications in Untargeted Clinical Lipidomics.
    DOI
    2021 Osborn, E. A., Ughi, G. J., Verjans, J. W., Piao, Z., Gerbaud, E., Albaghdadi, M., . . . Jaffer, F. A. (2021). Intravascular Molecular-Structural Assessment of Arterial Inflammation in Preclinical Atherosclerosis Progression. JACC: Cardiovascular Imaging, 14(11), 2265-2267.
    DOI Scopus4 WoS3 Europe PMC1
    2021 Pitt, B., Filippatos, G., Agarwal, R., Anker, S. D., Bakris, G. L., Rossing, P., . . . Ruilope, L. M. (2021). Cardiovascular Events with Finerenone in Kidney Disease and Type 2 Diabetes. NEW ENGLAND JOURNAL OF MEDICINE, 385(24), 2252-2263.
    DOI WoS412 Europe PMC302
    2021 Pitt, B., Filippatos, G., Agarwal, R., Anker, S. D., Bakris, G. L., Rossing, P., . . . Ruilope, L. M. (2021). Cardiovascular Events with Finerenone in Kidney Disease and Type 2 Diabetes. NEW ENGLAND JOURNAL OF MEDICINE, 385(24), 2252-2263.
    DOI WoS412 Europe PMC302
    2021 Einstein, A. J., Shaw, L. J., Hirschfeld, C., Williams, M. C., Villines, T. C., Better, N., . . . Paez, D. (2021). International Impact of COVID-19 on the Diagnosis of Heart Disease. JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 77(2), 173-185.
    DOI WoS93 Europe PMC88
    2021 Kudo, T., Lahey, R., Hirschfeld, C. B., Williams, M. C., Lu, B., Alasnag, M., . . . Mayoraz, M. (2021). Impact of COVID-19 Pandemic on Cardiovascular Testing in Asia: The IAEA INCAPS-COVID Study. JACC: Asia, 1(2), 187-199.
    DOI Scopus8
    2021 Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J. W., . . . Carneiro, G. (2021). Multi-centred Strong Augmentation via Contrastive Learning for Unsupervised Lesion Detection and Segmentation.. CoRR, abs/2109.01303.
    2021 Hirschfeld, C. B., Shaw, L. J., Williams, M. C., Lahey, R., Villines, T. C., Dorbala, S., . . . Masoli, O. (2021). Impact of COVID-19 on Cardiovascular Testing in the United States Versus the Rest of the World. JACC: Cardiovascular Imaging, 14(9), 1787-1799.
    DOI Scopus32 WoS16 Europe PMC29
    2021 Fouladzadeh, A., Dorraki, M., Min, K. K. M., Cockshell, M. P., Thompson, E. J., Verjans, J. W., . . . Abbott, D. (2021). The development of tumour vascular networks. Communications Biology, 4(1), 1111-1-1111-10.
    DOI Scopus11 WoS7 Europe PMC5
    2021 Uretsky, S., Aggarwal, N., van Heeswijk, R. B., Rajpal, S., Rowin, E., Taylor, M. D., . . . Shah, D. J. (2021). Standards for writing Society for Cardiovascular Magnetic Resonance (SCMR) endorsed guidelines, expert consensus, and recommendations: a report of the publications committee.. J Cardiovasc Magn Reson, 23(1), 129.
    DOI Scopus2 WoS2 Europe PMC2
    2021 Xie, Y., Zhang, J., Liao, Z., Verjans, J., Shen, C., & Xia, Y. (2021). Intra- and Inter-pair Consistency for Semi-supervised Gland Segmentation.. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 31, 894-905.
    DOI Scopus14 WoS5 Europe PMC1
    2021 Williams, M. C., Shaw, L., Hirschfeld, C. B., Maurovich-Horvat, P., Nørgaard, B. L., Pontone, G., . . . Sueldo, C. P. (2021). Impact of COVID-19 on the imaging diagnosis of cardiac disease in Europe. Open Heart, 8(2), e001681.
    DOI Scopus16 WoS11 Europe PMC14
    2021 Paige, E., Doyle, K., Jorm, L., Banks, E., Hsu, M. -P., Nedkoff, L., . . . Figtree, G. A. (2021). A Versatile Big Data Health System for Australia: Driving Improvements in Cardiovascular Health.. Heart, lung & circulation, 30(10), 1467-1476.
    DOI Scopus8 WoS6 Europe PMC4
    2021 Tian, Y., Pu, L. Z. C. T., Liu, Y., Maicas, G., Verjans, J. W., Burt, A. D., . . . Carneiro, G. (2021). Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning.. CoRR, abs/2101.03285.
    2021 Wang, D. D., Qian, Z., Vukicevic, M., Engelhardt, S., Kheradvar, A., Zhang, C., . . . Vannan, M. A. (2021). 3D printing, computational modeling, and artificial intelligence for structural heart disease. JACC: Cardiovascular Imaging, 14(1), 41-60.
    DOI Scopus63 WoS20 Europe PMC29
    2021 Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., . . . Xia, Y. (2021). Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection. IEEE Transactions on Medical Imaging, 40(3), 879-890.
    DOI Scopus180 WoS106 Europe PMC105
    2020 Montarello, N. J., Nelson, A. J., Verjans, J., Nicholls, S. J., & Psaltis, P. J. (2020). The role of intracoronary imaging in translational research. Cardiovascular Diagnosis and Therapy, 10(5), 1480-1507.
    DOI Scopus5 WoS4 Europe PMC2
    2020 Tian, Y., Maicas, G., Pu, L. Z. C. T., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy.. CoRR, abs/2006.14811.
    2020 Li, J., Thiele, S., Quirk, B. C., Kirk, R. W., Verjans, J. W., Akers, E., . . . McLaughlin, R. A. (2020). Ultrathin monolithic 3D printed optical coherence tomography endoscopy for preclinical and clinical use. Light: Science and Applications, 9(1), 124-1-124-10.
    DOI Scopus82 WoS56 Europe PMC18
    2020 Sengupta, P. P., Shrestha, S., Berthon, B., Messas, E., Donal, E., Tison, G. H., . . . Arnaout, R. (2020). Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council. JACC: Cardiovascular Imaging, 13(9), 2017-2035.
    DOI Scopus121 WoS82 Europe PMC58
    2020 Li, J., Thiele, S., Quirk, B. C., Kirk, R. W., Verjans, J. W., Akers, E., . . . McLaughlin, R. A. (2020). Ultrathin monolithic 3D printed optical coherence tomography endoscopy for preclinical and clinical use.. Light, science & applications, 9(1), 124.
    DOI
    2019 Liu, Y., Tian, Y., Maicas, G., Pu, L. Z. C. T., Singh, R., Verjans, J. W., & Carneiro, G. (2019). Photoshopping Colonoscopy Video Frames.. CoRR, abs/1910.10345.
    2019 Siegersma, K. R., Leiner, T., Chew, D. P., Appelman, Y., Hofstra, L., & Verjans, J. W. (2019). Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist. Netherlands Heart Journal, 27(9), 403-413.
    DOI Scopus56 WoS38 Europe PMC31
    2019 Verjans, J., & Leiner, T. (2019). Artificial intelligence for the general cardiologist. Netherlands Heart Journal, 27(9), 389-391.
    DOI Scopus2
    2018 Khokhar, K., Lau, D., Mahajan, R., Elliott, A., Stiles, M., Mishima, R., . . . Sanders, P. (2018). Aortic Pulse-Wave Velocity Assessment in Atrial Fibrillation. Heart, Lung and Circulation, 27, S63-S64.
    DOI
    2018 Snaauw, G., Gong, D., Maicas, G., Hengel, A. V. D., Niessen, W. J., Verjans, J., & Carneiro, G. (2018). End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging.. CoRR, abs/1810.10117.
    2017 Bouyoucef, S. E., Mercuri, M., Pascual, T. N. B., Allam, A. H., Vangu, M., Vitola, J. V., . . . Rajini, T. R. (2017). Nuclear cardiology practices and radiation exposure in Africa: Results from the IAEA Nuclear Cardiology Protocols Study (INCAPS). Cardiovascular Journal of Africa, 28(4), 229-234.
    DOI Scopus4 Europe PMC2
    2017 Bozhko, D., Osborn, E. A., Rosenthal, A., Verjans, J. W., Hara, T., Kellnberger, S., . . . Ntziachristos, V. (2017). Quantitative intravascular biological fluorescence-ultrasound imaging of coronary and peripheral arteries in vivo.. European heart journal cardiovascular Imaging, 18(11), 1253-1261.
    DOI Scopus26 WoS24 Europe PMC13
    2017 Rienks, M., Carai, P., Bitsch, N., Schellings, M., Vanhaverbeke, M., Verjans, J., . . . Papageorgiou, A. (2017). Sema3A promotes the resolution of cardiac inflammation after myocardial infarction. Basic Research in Cardiology, 112(4), 42-1-42-13.
    DOI Scopus60 WoS51 Europe PMC38
    2016 Verjans, J. W., Osborn, E. A., Ughi, G. J., Calfon Press, M. A., Hamidi, E., Antoniadis, A. P., . . . Jaffer, F. A. (2016). Targeted Near-Infrared Fluorescence Imaging of Atherosclerosis: Clinical and Intracoronary Evaluation of Indocyanine Green. JACC: Cardiovascular Imaging, 9(9), 1087-1095.
    DOI Scopus79 WoS66 Europe PMC43
    2016 Teske, A. J., & Verjans, J. W. (2016). Takotsubo cardiomyopathy – Stunning views on the broken heart. Netherlands Heart Journal, 24(9), 508-510.
    DOI Scopus3 WoS2 Europe PMC1
    2015 Ughi, G. J., Verjans, J., Fard, A. M., Wang, H., Osborn, E., Hara, T., . . . Tearney, G. J. (2015). Dual modality intravascular optical coherence tomography (OCT) and near-infrared fluorescence (NIRF) imaging: a fully automated algorithm for the distance-calibration of NIRF signal intensity for quantitative molecular imaging. International Journal of Cardiovascular Imaging, 31(2), 259-268.
    DOI Scopus49 WoS41 Europe PMC29
    2014 Jaffer, F. A., & Verjans, J. W. (2014). Molecular imaging of atherosclerosis: Clinical state-of-the-art. Heart, 100(18), 1469-1477.
    DOI Scopus30 WoS29 Europe PMC20
    2013 Verjans, J. W., & Jaffer, F. A. (2013). Biological imaging of atherosclerosis: Moving beyond anatomy. Journal of Cardiovascular Translational Research, 6(5), 681-694.
    DOI Scopus7 WoS7 Europe PMC4
    2011 Verjans, J. W. H., van de Borne, S. W. M., Hofstra, L., & Narula, J. (2011). Molecular Imaging of Myocardial Remodeling After Infarction. Methods in molecular biology (Clifton, N.J.), 680, 227-235.
    DOI Scopus6 Europe PMC4
    2011 Narula, N., Zaragoza, M. V., Sengupta, P. P., Li, P., Haider, N., Verjans, J., . . . Wallace, D. C. (2011). Adenine nucleotide translocase 1 deficiency results in dilated cardiomyopathy with defects in myocardial mechanics, histopathological alterations, and activation of apoptosis. JACC: Cardiovascular Imaging, 4(1), 1-10.
    DOI Scopus51 WoS48 Europe PMC37
    2010 Kenis, H., Zandbergen, H. R., Hofstra, L., Petrov, A. D., Dumont, E. A., Blankenberg, F. D., . . . Reutelingsperger, C. P. M. (2010). Annexin A5 uptake in ischemic myocardium: Demonstration of reversible phosphatidylserine externalization and feasibility of radionuclide imaging. Journal of Nuclear Medicine, 51(2), 259-267.
    DOI Scopus74 WoS65 Europe PMC63
    2010 Van Den Borne, S. W. M., Diez, J., Blankesteijn, W. M., Verjans, J., Hofstra, L., & Narula, J. (2010). Myocardial remodeling after infarction: The role of myofibroblasts. Nature Reviews Cardiology, 7(1), 30-37.
    DOI Scopus583 WoS534 Europe PMC421
    2010 Verjans, J., Wolters, S., Laufer, W., Schellings, M., Lax, M., Lovhaug, D., . . . Hofstra, L. (2010). Early molecular imaging of interstitial changes in patients after myocardial infarction: Comparison with delayed contrast-enhanced magnetic resonance imaging. Journal of Nuclear Cardiology, 17(6), 1065-1072.
    DOI Scopus44 WoS34 Europe PMC27
    2009 Zandbergen, H. R., Sharma, U. C., Gupta, S., Verjans, J. W. H., Van Den Borne, S., Pokharel, S., . . . Hofstra, L. (2009). Macrophage depletion in hypertensive rats accelerates development of cardiomyopathy. Journal of Cardiovascular Pharmacology and Therapeutics, 14(1), 68-75.
    DOI Scopus51 WoS48 Europe PMC36
    2008 van den Borne, S. W. M., Isobe, S., Verjans, J. W., Petrov, A., Lovhaug, D., Li, P., . . . Narula, J. (2008). Molecular Imaging of Interstitial Alterations in Remodeling Myocardium After Myocardial Infarction. Journal of the American College of Cardiology, 52(24), 2017-2028.
    DOI Scopus135 WoS119 Europe PMC87
    2008 Verjans, J. W. H., Lovhaug, D., Narula, N., Petrov, A. D., Indrevoll, B., Bjurgert, E., . . . Narula, J. (2008). Noninvasive Imaging of Angiotensin Receptors After Myocardial Infarction. JACC: Cardiovascular Imaging, 1(3), 354-362.
    DOI Scopus73 WoS63 Europe PMC40
    2008 Verjans, J., Hofstra, L., & Narula, J. (2008). Molecular imaging of interstitial alterations after myocardial infarction.. Journal of cardiovascular translational research, 1(3), 221-224.
    DOI
    2006 Petrucci, R. J., Truesdell, K. C., Carter, A., Goldstein, N. E., Russell, M. M., Dilkes, D., . . . Narula, J. (2006). Cognitive Dysfunction in Advanced Heart Failure and Prospective Cardiac Assist Device Patients. Annals of Thoracic Surgery, 81(5), 1738-1744.
    DOI Scopus31 WoS24 Europe PMC21
    2005 Hartung, D., Sarai, M., Petrov, A., Kolodgie, F., Narula, N., Verjans, J., . . . Narula, J. (2005). Resolution of apoptosis in atherosclerotic plaque by dietary modification and statin therapy. Journal of Nuclear Medicine, 46(12), 2051-2056.
    Scopus68 WoS66 Europe PMC48
    2003 Verjans, J. W., Narula, N., Loyd, A., Narula, J., & Vannan, M. A. (2003). Myocardial contrast echocardiography in acute myocardial infarction. Current Opinion in Cardiology, 18(5), 346-350.
    DOI Scopus2 WoS2 Europe PMC2
    2003 Kolodgie, F. D., Petrov, A., Virmani, R., Narula, N., Verjans, J. W., Weber, D. K., . . . Narula, J. (2003). Targeting of Apoptotic Macrophages and Experimental Atheroma with Radiolabeled Annexin V: A Technique with Potential for Noninvasive Imaging of Vulnerable Plaque. Circulation, 108(25), 3134-3139.
    DOI Scopus231 WoS198 Europe PMC146

  • Book Chapters
    Year Citation
    2023 Liao, Z., van den Hengel, A., & Verjans, J. W. (2023). Medical visual question answering. In A. C. Chang, A. Limon, R. Brisk, F. Lopez-Jimenez, & L. Y. Sun (Eds.), Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine (pp. 157-162). Elsevier.
    DOI
    2023 Dorraki, M., Sengupta, P. P., & Verjans, J. W. (2023). Artificial intelligence in echocardiography. In A. C. Chang, A. Limon, R. Brisk, F. Lopez-Jimenez, & L. Y. Sun (Eds.), Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine (pp. 179-184). Elsevier.
    DOI
    2022 Mehta, O., Liao, Z., Jenkinson, M., Carneiro, G., & Verjans, J. (2022). Machine Learning in Medical Imaging - Clinical Applications and Challenges in Computer Vision. In Artificial Intelligence in Medicine: Applications, Limitations and Future Directions (pp. 79-99). Springer Nature Singapore.
    DOI Scopus2
    2020 Rana, K., Nicholls, S. J., & Verjans, J. W. (2020). Mechanisms of the Vulnerable Atherosclerotic Plaque and Imaging. In R. Fitridge (Ed.), Mechanisms of Vascular Disease: a textbook for vascular specialists (3 ed., pp. 47-70). Cham, Switzerland: Springer.
    DOI
    2019 Verjans, J., Veldhuis, W. B., Carneiro, G., Wolterink, J. M., Išgum, I., & Leiner, T. (2019). Cardiovascular diseases. In E. R. Ranschaert, S. Morozov, & P. R. Algra (Eds.), Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks (pp. 167-185). Cham, Switzerland: Springer.
    DOI Scopus3
    2010 Strauss, H. W., Verjans, J. W. H., Zaret, B. L., & Narula, J. (2010). Radionuclide imaging of inflammation in atheroma. In Clinical Nuclear Cardiology (pp. 713-722). Elsevier.
    DOI
    2010 Strauss, H. W., Verjans, J. W. H., Zaret, B. L., & Narula, J. (2010). Radionuclide Imaging of Inflammation in Atheroma. In Clinical Nuclear Cardiology: State of the Art and Future Directions (pp. 713-722).
    DOI
    2007 Verjans, J., & Narula, J. (2007). Radionuclide Imaging. In The Vulnerable Atherosclerotic Plaque: Strategies for Diagnosis and Management (pp. 247-256). Wiley.
    DOI
    2007 Vannan, M. A., Ahsan, C., Verjans, J., Petrov, A., & Narula, J. (2007). Ultrasonic Detection of Coronary Disease. In The Vulnerable Atherosclerotic Plaque: Strategies for Diagnosis and Management (pp. 211-221). Wiley.
    DOI Scopus1

  • Conference Papers
    Year Citation
    2023 Phan, M. -H., Liao, Z., Verjans, J. W., & To, M. -S. (2023). Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation.. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. F. Syeda-Mahmood, & R. H. Taylor (Eds.), MICCAI (10) Vol. 14229 (pp. 56-65). Springer.
    2023 Tian, Y., Pang, G., Liu, Y., Wang, C., Chen, Y., Liu, F., . . . Carneiro, G. (2023). Unsupervised Anomaly Detection in Medical Images with a Memory-Augmented Multi-level Cross-Attentional Masked Autoencoder.. In X. Cao, X. Xu, I. Rekik, Z. Cui, & X. Ouyang (Eds.), MLMI@MICCAI (2) Vol. 14349 (pp. 11-21). Springer.
    2022 Snaauw, G., Sasdelli, M., Maicas, G., Lau, S., Verjans, J., Jenkinson, M., & Carneiro, G. (2022). Mutual Information Neural Estimation for Unsupervised Multi-Modal Registration of Brain Images.. In EMBC (pp. 3510-3513). IEEE.
    2022 Tian, Y., Pang, G., Liu, F., Liu, Y., Wang, C., Chen, Y., . . . Carneiro, G. (2022). Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection. In Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13433 (pp. 88-98). Online: Springer Link.
    DOI Scopus4 WoS1
    2022 Snaauw, G., Sasdelli, M., Maicas, G., Lau, S., Verjans, J., Jenkinson, M., & Carneiro, G. (2022). Mutual information neural estimation for unsupervised multi-modal registration of brain images. In 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2022-July (pp. 3510-3513). Online: IEEE.
    DOI Scopus2
    2022 Tian, Y., Pang, G., Liu, F., Liu, Y., Wang, C., Chen, Y., . . . Carneiro, G. (2022). Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection.. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), MICCAI (3) Vol. 13433 (pp. 88-98). Springer.
    2021 Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. -H., Verjans, J. W., . . . Carneiro, G. (2021). Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images.. In M. D. Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), MICCAI (5) Vol. 12905 (pp. 128-140). Springer.
    2021 Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W., & Carneiro, G. (2021). Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning.. In ICCV (pp. 4955-4966). IEEE.
    2021 Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S. -H., Verjans, J. W., . . . Carneiro, G. (2021). Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images. In Proceedings of the 24th Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), as publisghed in Lecture Notes in Computer Science Vol. 12905 (pp. 128-140). Cham, Switzerland: Springer.
    DOI Scopus29 WoS16
    2021 Shen, H., Liao, K., Liao, Z., Doornberg, J., Qiao, M., Van Den Hengel, A., & Verjans, J. W. (2021). Human-AI interactive and continuous sensemaking: A case study of image classification using scribble attention maps. In Proceedings of the Conference on Human Factors in Computing Systems (CHI'21) (pp. 1-8). New York, NY: Association for Computing Machinery.
    DOI Scopus4 WoS3
    2021 Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W., & Carneiro, G. (2021). Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2021) Vol. abs/2101.10030 (pp. 4955-4966). virtual online: IEEE.
    DOI Scopus122 WoS76
    2020 Zhang, J., Xie, Y., Liao, Z., Verjans, J., & Xia, Y. (2020). EfficientSeg: A Simple But Efficient Solution to Myocardial Pathology Segmentation Challenge. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12554 LNCS (pp. 17-25). Switzerland: Springer International Publishing.
    DOI Scopus8
    2020 Xie, Y., Zhang, J., Liao, Z., Verjans, J., Shen, C., & Xia, Y. (2020). Pairwise Relation Learning for Semi-supervised Gland Segmentation.. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, . . . L. Joskowicz (Eds.), MICCAI (5) Vol. 12265 (pp. 417-427). Switzerland: Springer Nature.
    DOI
    2020 Liao, Z., Wu, Q., Shen, C., Van Den Hengel, A., & Verjans, J. (2020). AIML at VQA-Med 2020: Knowledge inference via a skeleton-based sentence mapping approach for medical domain visual question answering. In L. Cappellato, C. Eickhoff, N. Ferro, & A. Névéol (Eds.), Proceedings of the 11th International Conference of the CLEF Initiative (CLEF 2020), as published in CEUR Workshop Proceedings Vol. 2696 (pp. 1-14). online: CEUR-WS.
    Scopus6
    2020 Liao, Z., Liu, L., Wu, Q., Teney, D., Shen, C., Van Den Hengel, A., & Verjans, J. (2020). Medical data inquiry using a question answering model. In Proceedings: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1490-1493). online: IEEE.
    DOI Scopus7 WoS3
    2020 Liu, Y., Tian, Y., Maicas Suso, G., Zorron Cheng Tao Pu, L., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Photoshopping colonoscopy video frames. In Proceedings of the IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1-5). Iowa City, Iowa, USA: IEEE.
    DOI Scopus5 WoS5
    2020 Tian, Y., Maicas Suso, G., Zorron Cheng Tao Pu, L., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Few-shot anomaly detection for polyp frames from colonoscopy. In Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2020), as published in Lecture Notes in Computer Science Vol. 12266 (pp. 274-284). Switzerland: Springer Nature.
    DOI Scopus15
    2020 Xie, Y., Zhang, J., Liao, Z., Verjans, J., Shen, C., & Xia, Y. (2020). Pairwise Relation Learning for Semi-supervised Gland Segmentation.. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, . . . L. Joskowicz (Eds.), MICCAI (5) Vol. 12265 (pp. 417-427). Switzerland: Springer Nature.
    DOI Scopus22
    2019 Snaauw, G., Gong, D., Maicas, G., van den Hengel, A., Niessen, W. J., Verjans, J., & Carneiro, G. (2019). End-to-end diagnosis and segmentation learning from cardiac magnetic resonance imaging. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019): Proceedings Vol. 2019-April (pp. 802-805). online: IEEE.
    DOI Scopus19 WoS12
    2018 Siegersma, K. R., Zreik, M., Coroller, T. P., Dweck, M. R., Everett, R. J., Treibel, T., . . . Verjans, J. W. H. (2018). Prediction of the risk of valve surgery and adverse events in patients with aortic stenosis: myocardial tissue characterization with radiomics. In EUROPEAN HEART JOURNAL Vol. 39 (pp. 1129-1130). Munich, GERMANY: OXFORD UNIV PRESS.
    2018 Siegersma, K. R., Zreik, M., Coroller, T., Dweck, M. R., Everett, R. J., Treibel, T., . . . Verjans, J. W. H. (2018). Discrimination of fibrotic myocardium from healthy myocardium patients with aortic stenosis: a radiomics approach with machine learning models. In EUROPEAN HEART JOURNAL Vol. 39 (pp. 971-972). Munich, GERMANY: OXFORD UNIV PRESS.
    2017 Bozhko, D., Osborn, E. A., Rosenthal, A., Verjans, J. W. H., Hara, T., McCarthy, J. R., . . . Ntziachristos, V. (2017). Quantitative intravascular fluorescence-ultrasound imaging in vivo. In Optics InfoBase Conference Papers Vol. Part F63-OMP 2017. OSA.
    DOI
    2015 Verjans, J. W., Osborn, E. A., Ughi, G. J., Antoniadis, A. P., Papafaklis, M. I., Libby, P., . . . Jaffer, F. A. (2015). First clinical and intracoronary evaluation of indocyanine green for targeted intravascular near-infrared fluorescence imaging of high-risk atherosclerotic plaques. In EUROPEAN HEART JOURNAL Vol. 36 (pp. 1194). London, ENGLAND: OXFORD UNIV PRESS.
    2014 Verjans, J., Osborn, E., Ughi, G., Kessinger, C. W., Mallidi, S., Hasan, T., . . . Jaffer, F. A. (2014). First-in-Human Evaluation of Indocyanine Green for Targeted Optical Imaging of Atherosclerosis: Implications for Inflamed, High-Risk Plaques. In CIRCULATION Vol. 130 (pp. 4 pages). LIPPINCOTT WILLIAMS & WILKINS.
    2007 Verjans, J. W., Wolters, S. L., Lax, M., Laufer, W., Boersma, H., Kemerink, G. K., . . . Hofstra, L. (2007). Imaging alpha v beta 3/beta 5 integrin upregulation in patients after myocardial infarction. In CIRCULATION Vol. 116 (pp. 740-741). Orlando, FL: LIPPINCOTT WILLIAMS & WILKINS.
    WoS3
    2007 Van den Borne, S. W., Isobe, S., Verjans, J., Petrov, A., Lovhaug, D., Li, P., . . . Narula, J. (2007). Molecular imaging of post-infarction cardiac remodeling and effects of anti-angiotensi in therapy. In CIRCULATION Vol. 116 (pp. 289-290). Orlando, FL: LIPPINCOTT WILLIAMS & WILKINS.
    WoS5
    2005 Li, P., Tonti, G., Verjans, J., Pedrizzetti, G., Mehta, H., Appleby, S., . . . Vannen, M. A. (2005). Measurement of apical torsion in mitochrondrial cardiomyopathy using a novel B-mode, automated tracking algorithm. In JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY Vol. 45 (pp. 305A). Orlando, FL: ELSEVIER SCIENCE INC.
    WoS3
    2004 Verjans, J. W., Haider, N., Li, P., Narula, N., Brittin, R., Gabe, J. D., . . . Vannan, M. A. (2004). Targeted ultrasound imaging of apoptosis in acute myocardial injury with Annexin-A5 microspheres. In CIRCULATION Vol. 110 (pp. 509). New Orleans, LA: LIPPINCOTT WILLIAMS & WILKINS.
    WoS1

  • Conference Items
    Year Citation
    2022 Dorraki, M., Liao, Z., Abbott, D., Psaltis, P. J., Baker, E., Bidargaddi, N., . . . Verjans, J. W. (2022). Cardiovascular disease risk prediction via machine learning using mental health data. Poster session presented at the meeting of EUROPEAN HEART JOURNAL. OXFORD UNIV PRESS.
    2020 Franke, K., Loffler, K. A., Nicholls, S. J., Anderson, C. S., Cowan, B. R., McEvoy, R. D., . . . Verjans, J. W. (2020). Effects of CPAP treatment on cardiac structure and function in individuals with obstructive sleep apnea and cardiovascular disease: a prospective 6-month randomised control trial. Poster session presented at the meeting of Abstracts of the European Society of Cardiology Congress (ESC 2020) as published in the European Heart Journal. Virtual Online: Oxford University Press.
    DOI
    2018 Siegersma, K. R., Zreik, M., Coroller, T. P., Dweck, M. R., Everett, R. J., Treibel, T., . . . Verjans, J. W. H. (2018). P5463Prediction of the risk of valve surgery and adverse events in patients with aortic stenosis: myocardial tissue characterization with radiomics. Poster session presented at the meeting of European Heart Journal. Oxford University Press (OUP).
    DOI
    2018 Siegersma, K. R., Zreik, M., Coroller, T., Dweck, M. R., Everett, R. J., Treibel, T., . . . Verjans, J. W. H. (2018). P4686Discrimination of fibrotic myocardium from healthy myocardium patients with aortic stenosis: a radiomics approach with machine learning models. Poster session presented at the meeting of European Heart Journal. Oxford University Press (OUP).
    DOI
    2016 Osborn, E. A., Ughi, G. J., Verjans, J. W., Gerbaud, E., Takx, R. A., Tawakol, A., . . . Jaffer, F. A. (2016). In Vivo Plaque Inflammation and Endothelial Permeability Independently Predict Atherosclerosis Progression: A Serial Multimodality Imaging Study. Poster session presented at the meeting of ARTERIOSCLEROSIS THROMBOSIS AND VASCULAR BIOLOGY. LIPPINCOTT WILLIAMS & WILKINS.
    WoS1
    2016 Verjans, J. W., Ughi, G. J., Osborn, E. A., Gerboud, E. M., Takx, R. A., Tawakol, A., . . . Jaffer, F. A. (2016). Multimodality intravascular molecular imaging of inflammation and endothelial permeability independently predicts plaque progression over time. Poster session presented at the meeting of EUROPEAN HEART JOURNAL. Rome, ITALY: OXFORD UNIV PRESS.

  • Curated or Produced Public Exhibition or Events
    Year Citation
    2021 Hajdu, T., Van Den Hengel, A., Sherrah, J., Dalby, P., & Verjans, J. (2021). Adelaide Festival of Ideas: Art of Artificial Intelligence (No. Of Pieces: 1 hour) [panel discussion]. Adelaide: https://adelaidefestivalofideas.com.au.

  • Preprint
    Year Citation
    2024 Phan, V. M. H., Xie, Y., Qi, Y., Liu, L., Liu, L., Zhang, B., . . . Verjans, J. W. (2024). Decomposing Disease Descriptions for Enhanced Pathology Detection: A
    Multi-Aspect Vision-Language Pre-training Framework.
    2024 Chowdhury, T. F., Liao, K., Phan, V. M. H., To, M. -S., Xie, Y., Hung, K., . . . Liao, Z. (2024). CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation.
    2023 Zhang, Z., Qi, X., Zhang, B., Wu, B., Le, H., Jeong, B., . . . Hartley, R. (2023). SegReg: Segmenting OARs by Registering MR Images and CT Annotations.

  • Current Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2024 Principal Supervisor Pre-trained multimodel model for integrated healthcare decision support Doctor of Philosophy Doctorate Full Time Ms Nanyu Dong
    2024 Principal Supervisor Advancing Medical Image Analysis with Insights from Large-scale Foundation Models Doctor of Philosophy Doctorate Full Time Yunxiang Liu
    2023 Principal Supervisor Photonics and Machine learning approaches to Detect Environment, Physiology and Disease Doctor of Philosophy Doctorate Full Time Ms Madeleine Cochrane
    2023 Principal Supervisor Learning to Reason and Generalise Using Multimodal Approaches Doctor of Philosophy Doctorate Full Time Mr Luke Thomas Heffernan
    2023 Co-Supervisor Explainable and Semantically Meaningful Deep Learning Models for Medical Risk Prediction and Diagnostics Doctor of Philosophy Doctorate Full Time Mr Townim Faisal Chowdhury
    2023 Principal Supervisor Causal discovery and Out-of-Distribution generalization: sampling from posterior over causal graphs Doctor of Philosophy Doctorate Full Time Mr Nadhir Hassen
    2019 Co-Supervisor Lipidomics in the Study of Atherosclerosis Doctor of Philosophy Doctorate Full Time Mr Jake White

  • Past Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2019 - 2022 Co-Supervisor Anomaly Detection in Computer Vision and Medical Imaging Doctor of Philosophy Doctorate Full Time Mr Yu Tian

  • Board Memberships
    Date Role Board name Institution name Country
    2021 - ongoing Advisory Board Member AI in Health Hub SA Government Australia
    2021 - ongoing Board Member Innovation Board Netherlands Heart Registry Australia
    2019 - ongoing Advisory Board Member Big Data Advisory Board Australian Cardiovascular Alliance Australia

  • Editorial Boards
    Date Role Editorial Board Name Institution Country
    2023 - ongoing Board Member European Heart Journal: Digital Health University of Adelaide Australia
    2023 - ongoing Board Member Frontiers in Cardiology University of Adelaide Australia
    2016 - ongoing Associate Editor Netherlands Heart Journal Springer Australia
    2015 - ongoing Associate Editor Netherlands Heart Journal - Netherlands
  • Position: Associate Professor
  • Email: johan.verjans@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning
  • Org Unit: Australian Institute for Machine Learning - Operations
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