The purpose of the Applied Imagery Pattern Recognition (AIPR) annual workshops is to bring together researchers from government, industry, and academia in an elegant setting conducive to technical interchange across a broad range of disciplines. The papers span a range of topics, from research to fielded systems and provide to scientists, developers, and managers alike, a broad vision of the applicability of image analysis and machine learning technologies.
AIPR continues the half-century of success and tradition in pioneering new topics in applied image and visual understanding. The current worldwide pandemic focuses our collective attention on artificial intelligence (AI) in medicine and biomedicine to improve healthcare. Healthcare systems around the world are being challenged and experiencing a large influx of patients requiring intensive monitoring. Researchers are working on near-term and long-term projects to bring relief to clinical providers with enhanced automation capability to assist in patient monitoring, rapid diagnosis, and drug discovery. Large medical databases are offering new opportunities to accelerate the adoption of AI and improve medical outcomes. The 2021 IEEE AIPR Workshop will explore artificial intelligence in medicine, healthcare, and neuroscience.
As of August 15th, 2021, the AIPR 2021 Workshop will be held virtually. This was a difficult decision, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic have made it unlikely that we would be able to guarantee a safe environment for all attendees at a large in-person event.
Our goal remains to provide a workshop of the highest quality - we aim to take full advantage of the virtual platform to enhance inclusivity, with an emphasis on showcasing computer vision and medical AI.
Thank you for your patience and understanding in these unprecedented times.
Registration now open!
Extended deadline for abstracts: September 3rd
Author notifications: September 10th
Presentation upload: October 1st
In addition to papers on regular AIPR topics in applied imagery, the Workshop Committee invites papers that address all aspects of medical and biomedical AI, development of novel tools, methodologies, algorithms, theory, and mechanisms for biomedicine, healthcare, and neuroscience. Theme topics include, but are not limited to, the following:
- AI-based medical imaging for COVID-19 detection, outcome prediction, or monitoring
- Deep learning and AI for medicine, computer aided diagnosis, biology, drug discovery
- Medical image classification, segmentation, and registration
- Connectomics, brain imaging, fMRI, neural circuitry, neuropathologies
- Multiscale biological and biomedical signal and image analysis
- Deep neural networks for pathology, radiology, and microscopy
- Robotics technologies for medical and biomedical applications
- Biomedical analysis for clinical imaging and informatics
- Data mining and image retrieval for biomedical imagery
- Personalized healthcare, electronic health records, translational and precision medicine
- Geospatial epidemiology and healthcare
- Pattern recognition for early identification of viral contagions
- Patient monitoring, wearable devices, and multi-sensor multimodal diagnostics
- Regional healthcare lessons learned in pandemics
- Advances in statistical image processing
- Novel uses of differentiable programming in developing AI systems
- Ubiquitous sensors and their impact on biomedical measurements
- Conducting medical imaging science with urgency
Deadline for abstracts extended! Our submission system is open and will be accepting abstract submissions until September 3. We will be reviewing abstracts and sending acceptance decisions from now until September 10. The Workshop will include oral and poster presentations, several keynote talks that provide in-depth overviews of the fields, and a special session. Written papers will be required (due after the workshop) and will be indexed in IEEEXplore. AIPR 2021, the 50th annual workshop, is sponsored by the IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence, and organized by the AIPR Workshop Committee with generous support from sponsors. Updates and additional information can be found at www.aipr-workshop.org.
Accepted Invited Speakers:
Dr. Richard Maude, Associate Professor Centre for Tropical Medicine and Global Health
Professor Maude is Head of the Epidemiology Department at Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand and Associate Professor in Tropical Medicine at the University of Oxford, Honorary Consultant Physician at the John Radcliffe Hospital in Oxford and a Visiting Scientist at Harvard TH Chan School of Public Health, Harvard University, Boston, USA. He has worked at Mahidol-Oxford Tropical Medicine Research Unit since 2007.
His research combines clinical studies, descriptive epidemiology and mathematical modelling of human diseases in South and Southeast Asia. His areas of interest include spatiotemporal epidemiology, GIS mapping, disease surveillance, health policy, pathogen genetics and population movement with a focus on malaria, dengue, novel pathogens including COVID-19 and environmental health.
He is a founding member of ThaiGISNet and GroupMappers, a Fellow of the Royal Geographical Society and Royal College of Physicians in the UK, MORU representative for the Asia-Pacific Malaria Elimination Network, board member of the Bill and Melinda Gates Foundation Malaria Modelling Consortium and member of the COVID-19 Mobility Data Network.
Professor Maude is Assistant Director of Graduate Studies at the Nuffield Department of Medicine and co-chairs the MORU Postgraduate Committee. He runs training courses for government disease control programmes and academics on epidemiology, data analysis, modelling and GIS.
Dr. Patricia Brennan, Director, National Library of Medicine, National Institute of Health
Patricia Flatley Brennan, RN, PhD, is the Director of the National Library of Medicine (NLM), one of the 27 Institutes and Centers of the National Institutes of Health (NIH). NLM, the center for biomedical and health data science research, is the world’s largest biomedical library and the producer of digital information services used by scientists, health professionals and members of the public worldwide.
Since assuming the directorship in August 2016, Dr. Brennan has positioned the Library to be the hub of data science at NIH and a national and international leader in the field. She spearheaded the development of a new strategic plan that envisions NLM a platform for biomedical discovery and data-powered health. Leveraging NLM’s heavily used data and information resources, intramural research, and extramural research and training programs, Brennan aims for NLM to accelerate data driven discovery and health, engage with new users in new ways, and develop the workforce for a data-driven future.
Her professional accomplishments reflect her background, which unites engineering, information technology, and clinical care to improve the public health and ensure the best possible experience in patient care.
Dr. Brennan came to NIH from the University of Wisconsin-Madison, where she was the Lillian L. Moehlman Bascom Professor at the School of Nursing and College of Engineering. She also led the Living Environments Laboratory at the Wisconsin Institutes for Discovery, which develops new ways for effective visualization of high dimensional data.
She received a master of science in nursing from the University of Pennsylvania and a PhD in industrial engineering from the University of Wisconsin-Madison. Following seven years of clinical practice in critical care nursing and psychiatric nursing, Dr. Brennan held several academic positions at Marquette University, Milwaukee; Case Western Reserve University, Cleveland; and the University of Wisconsin-Madison.
A past president of the American Medical Informatics Association, Dr. Brennan was elected to the Institute of Medicine of the National Academy of Sciences (now the National Academy of Medicine) in 2001. She is a fellow of the American Academy of Nursing, the American College of Medical Informatics, and the New York Academy of Medicine.
In 2020, Dr. Brennan was inducted into the American Institute for Medical and Biological Engineering (AIMBE). The AIMBE College of Fellows is among the highest professional distinctions accorded to a medical and biological engineer.
Dr. Michael Hawrylycz, Allen Institute for Brain Science
Mike Hawrylycz, Ph.D., is an Investigator and Director of Data Science and Informatics at the Allen Institute for Brain Science, where he has been since its founding in 2003. His work has been in the development of infrastructure for and analysis of large-scale data in neuroscience, particularly for digital atlases of gene expression in the brain. Recent work includes development of the BRAIN Initiative Cell Census Network data center, Brain Cell Data Center (BCDC, www.biccn.org), for single cell data in the mammalian brain, including neuronal morphology, high throughput transcriptomics and related tools. This work includes addressing imaging processing problems of registration and segmentation in high-throughput neuroscience, and its relationship to neuroanatomy. Prior to joining the Allen Institute, he worked on the NHGRI ENCODE project for mapping genomic regulatory elements and maintains an active interest in epigenetics and its relationship to cell types.
Dr. Tara Schwetz, White House Office of Science and Technology Policy, Associate Deputy Director, National Institute of Health
Tara A. Schwetz, Ph.D. is the Associate Deputy Director, National Institutes of Health (NIH). Prior to assuming this role, Dr. Schwetz was the Chief of the Strategic Planning and Evaluation Branch in the Office of the Director at the National Institute of Allergy and Infectious Diseases (NIAID). During her tenure at NIAID, she led several efforts, including conducting an evaluation of the Centers of Excellence for Influenza Research and Surveillance to facilitate evidence-based decision-making and developing the NIAID Strategic Plan for Tuberculosis Research.Previously, Dr. Schwetz served as the Senior Advisor to the Principal Deputy Director of NIH, where she coordinated efforts such as Reimagine HHS, the NIH rigor and reproducibility activities, and the NIH-Wide Strategic Plan. Dr. Schwetz also served in the dual role of the NIH Environmental influences on Child Health Outcomes Interim Associate Program Director and the Special Assistant to the DEPD. Prior to these roles, she was a Health Science Policy Analyst at the National Institute of Neurological Disorders and Stroke, where she helped develop the National Pain Strategy. Dr. Schwetz started her career at NIH as an AAAS Science and Technology Policy Fellow at the National Institute of Nursing Research. She received a BS in biochemistry with honors from Florida State University and a PhD in biophysics from the University of South Florida, followed by a postdoctoral fellowship at Vanderbilt University. Dr. Schwetz has received numerous awards, including fellowships from the American Cancer Society, the American Heart Association, and NIH.
Dr. Fleming Y. M. Lure, Chief Product Officer, MS Technologies Corp
Dr. Fleming Y. M. Lure is currently a Chief Product Officer, MS Technologies Corp, Rockville, MD, since 2012. He had worked and established a team at Kodak Health Imaging to develop computer aided detection (CAD) including image processing, machine learning, artificial intelligence to detect breast cancers on mammograms. Later, he has co-founded a company Deus Technologies Corp and served as VP of Research and Development to develop CAD products to automatically detect lung cancers and tuberculosis on medical images in 1997. Deus was later acquired by investors led by board of director of P&G to form Riverain Technologies. He then joined Guardian Technologies Corp to develop the CAD product to detect acid fast bacilli of tuberculosis on sputum smear microscopy. This has helped Guardian to be listed in NASDAQ (symbol: APVS) in early 2010. Dr. Fleming Lure received his Ph.D. in Electrical Engineering, Pennsylvania State University.
For over 20 years, he has conducted research on computer-aided diagnosis, including computer vision, machine learning, image processing, and deep learning in the areas of radiological and pathological images for breast cancer, lung cancer, tuberculosis, Alzheimer’s disease, COVID-19, and others. Recently, he has also conducted research and development on hand-held low-power radar and artificial intelligence for the detection of fall of older adults and analysis of gait for the risk estimation of Alzheimer’s disease. Dr. Lure has led the team that developed the first FDA pre-market approved (PMA) CAD systems for early-stage lung cancer detection in radiological images. He is PI/PD for over 12 SBIR/STTR Phase I and II projects funded by NIH and DoD and has published over 90 papers in journals and conference proceedings.
Tommi White (University of Missouri) and Alberto Bartesaghi (Duke University)
Cryo-electron microscopy, cryo-EM, Single particle analysis, SPA, single particle reconstruction, SPR, cryo-electron tomography, cryoET, particle detection, particle picking, image regularization, semantic tomogram segmentation, continuous heterogeneity, transformation invariant classification, latent space representations.
Jing Wang (University of Missouri) and Ilker Ozden (University of Missouri)
Methods for analyzing neural signals, large scale neural data, neural dynamics, functional imaging, neural circuitry, fMRI, EEG, electrophysiology, population spiking, Machine learning, deep learning, neural networks, computation models of the brain, connectomics, disease models and neuropathologies
Digital pathology image analytics
AIPR 2021 Conference Chairs: Josh Harguess (MITRE) and Chris Ward (MITRE)