Lena Maier-Hein is the head of the Computer Assisted Medical Interventions (CAMI) department at the German Cancer Research Center (DKFZ) in Heidelberg, Germany. Her research focuses on Surgical Data Science and rankings of biomedical challenges.

Lena Maier-Hein is the head of the Computer Assisted Medical Interventions (CAMI) department at the German Cancer Research Center (DKFZ) in Heidelberg, Germany. Her research focuses on Surgical Data Science and rankings of biomedical challenges.

Lena Maier-Hein: What does it mean to win a Biomedical Challenge?

Russ Taylor

Russ Taylor is the father of robotic surgery. Hi is the John C. Malone Professor in the Department of Computer Science, and the director of the Laboratory for Computational Sensing and Robotics. His research has focused on all aspects of computer-integrated interventional medicine. Broadly, this research has included:  Medical robotics Medical imaging &amp; modeling and Complete systems for surgical assistance, image-guided surgery, and "Surgical CAD/CAM".  An underlying theme has been the basic insight that information-based technologies can have just as profound an impact on computer-integrated medicine as it has had on computer-integrated manufacturing.

Russ Taylor is the father of robotic surgery. Hi is the John C. Malone Professor in the Department of Computer Science, and the director of the Laboratory for Computational Sensing and Robotics. His research has focused on all aspects of computer-integrated interventional medicine. Broadly, this research has included: <ul> <li>Medical robotics</li> <li>Medical imaging &amp; modeling and</li> <li>Complete systems for surgical assistance, image-guided surgery, and "Surgical CAD/CAM".</li> </ul> An underlying theme has been the basic insight that information-based technologies can have just as profound an impact on computer-integrated medicine as it has had on computer-integrated manufacturing.

Russ Taylor: The role of AI in Robotic Surgery

Ilker is interested in the extraction of relevant information from three dimensional (3D) medical images by developing state of the art computational algorithms for image guided surgery and therapy applications. The main objective of his research is to study and model medical procedures and introduce advanced computer integrated solutions to improve their quality, efficiency, and safety.

Ilker is interested in the extraction of relevant information from three dimensional (3D) medical images by developing state of the art computational algorithms for image guided surgery and therapy applications. The main objective of his research is to study and model medical procedures and introduce advanced computer integrated solutions to improve their quality, efficiency, and safety.

Ilker Hacihaliloglu: Ultrasound for all

Frank Xu

Frank (Yanwu) Xu, is an Intelligent Healthcare Scientist (research lead) at Baidu, an Adjunct Professor at Ningbo Institute of Materials Technology &amp; Engineering, the Chinese Academy of Sciences (CAS), and an Adjunct Principal Investigator at Singapore Eye Research Institute. Frank is also serving the World Health Organization (WHO) as a technical advisory group member of Digital Health and an expert group member of Data Principles and Sharing Policies.

<a href="https://scholar.google.com.sg/citations?user=0jP8f7sAAAAJ&amp;hl=en" rel="nofollow" target="_blank">Frank (Yanwu) Xu</a>, is an Intelligent Healthcare Scientist (research lead) at Baidu, an Adjunct Professor at Ningbo Institute of Materials Technology &amp; Engineering, the Chinese Academy of Sciences (CAS), and an Adjunct Principal Investigator at Singapore Eye Research Institute. Frank is also serving the World Health Organization (WHO) as a technical advisory group member of Digital Health and an expert group member of Data Principles and Sharing Policies.

Frank Xu: Baidu's AI, WHO's Digital Health & other stories

Lorenzo Righetto is an associate Editor of Nature Communications where he handles manuscripts in the area of digital medicine and computational health. Lorenzo joined Nature Communications in January 2020. Lorenzo is based in the London office.

<a href="https://www.nature.com/ncomms/editors" rel="nofollow" target="_blank">Lorenzo Righetto</a> is an associate Editor of Nature Communications where he handles manuscripts in the area of digital medicine and computational health. Lorenzo joined Nature Communications in January 2020. Lorenzo is based in the London office.

Lorenzo Righetto: Publishing MICCAI research into Nature Communications

Karsten Ridder is a practicing radiologist from Dortmund, Germany with a special focus on Women's Health Imaging and Cardiovascular Imaging. He received numerous awards for his clinical research and innovation, including German Medical Award for Innovation in 2021. 

<a href="https://www.linkedin.com/in/dr-med-karsten-ridder-42162a11a/" rel="nofollow" target="_blank">Karsten Ridder</a> is a practicing radiologist from Dortmund, Germany with a special focus on Women's Health Imaging and Cardiovascular Imaging. He received numerous awards for his clinical research and innovation, including German Medical Award for Innovation in 2021.

Karsten Ridder: Communication is key for AI-ready Healthcare

Prof. Shuo Li is the Founding Scientific Director of Digital Imaging Group at London, Canada. He is also the general chair of MICCAI 2022 that will happen in Singapore.

<a href="http://www.digitalimaginggroup.ca/members/shuo.php" rel="nofollow" target="_blank">Prof. Shuo Li</a> is the Founding Scientific Director of Digital Imaging Group at London, Canada. He is also the general chair of MICCAI 2022 that will happen in Singapore.

Shuo Li: Will MICCAI 2022 be virtual?

Julia Schnabel is the Professor for Computational Imaging and AI in Medicine at TUM (TUM Liesel Beckmann Distinguished Professorship), jointly with Helmholtz Center Munich (Helmholtz Distinguished Professorship). Her research focuses on intelligent imaging solutions and computer aided evaluation, including complex motion modelling, image reconstruction, image quality control, image segmentation and classification, applied to multi-modal, quantitative and dynamic imaging. She is the co-general chair of MICCAI 2024, the first MICCAI in Africa. She often Tweets @ja_schnabel.

<a href="https://www.professoren.tum.de/en/schnabel-julia" rel="nofollow" target="_blank">Julia Schnabel</a> is the Professor for Computational Imaging and AI in Medicine at TUM (TUM Liesel Beckmann Distinguished Professorship), jointly with Helmholtz Center Munich (Helmholtz Distinguished Professorship). Her research focuses on intelligent imaging solutions and computer aided evaluation, including complex motion modelling, image reconstruction, image quality control, image segmentation and classification, applied to multi-modal, quantitative and dynamic imaging. She is the co-general chair of MICCAI 2024, the <a href="https://www.bcn-aim.org/miccai-2024-first-time-in-marrakesh-morocco/" rel="nofollow" target="_blank">first MICCAI in Africa</a>. She often Tweets <a href="https://twitter.com/ja_schnabel" rel="nofollow" target="_blank">@ja_schnabel</a>.

Julia Schnabel: MICCAI goes to Africa

Anant Madabhushi is the Donnell Institute Professor of Biomedical Engineering at Case Western Reserve University (CWRU) in Cleveland and director of the university's Center for Computational Imaging and Personalized Diagnostics (CCIPD). He is a Research Scientist at the Louis Stokes Cleveland Veterans Administration (VA) Medical Center and has affiliate appointments both at University Hospitals and Cleveland Clinic. He holds secondary appointments in the departments of Urology, Radiology, Pathology, Radiation Oncology, General Medical Sciences, Computer &amp; Data Sciences, and Electrical, Computer and Systems Engineering at CWRU. We talked about his research on translation of AI to clinical oncology. He tweets regularly @anantm.

<a href="https://case.edu/medicine/ccir/faculty/anant-madabhushi" rel="nofollow" target="_blank">Anant Madabhushi</a> is the Donnell Institute Professor of Biomedical Engineering at <a href="https://en.wikipedia.org/wiki/Case_Western_Reserve_University" rel="nofollow" target="_blank">Case Western Reserve University</a> (CWRU) in <a href="https://en.wikipedia.org/wiki/Cleveland" rel="nofollow" target="_blank">Cleveland</a> and director of the university's Center for Computational Imaging and Personalized Diagnostics (CCIPD). He is a Research Scientist at the Louis Stokes Cleveland Veterans Administration (VA) Medical Center and has affiliate appointments both at University Hospitals and Cleveland Clinic. He holds secondary appointments in the departments of Urology, Radiology, Pathology, Radiation Oncology, General Medical Sciences, Computer &amp; Data Sciences, and Electrical, Computer and Systems Engineering at CWRU. We talked about his research on translation of AI to clinical oncology. He tweets regularly <a href="https://twitter.com/anantm" rel="nofollow" target="_blank">@anantm</a>.

Anant Madabhushi: When MICCAI scientist meets Real Clinicians

Leo Joskowicz is a pioneer of Computer Assisted Intervention and the President of MICCAI society. He is a professor at the School of Engineering and Computer Science at the Hebrew University of Jerusalem. In this episode, we explore his interest in Geometry, Shape and making MICCAI society a home to all scientists working in medical imaging problems, no matter their geographical location.

<a href="https://elsc.huji.ac.il/people-directory/faculty-members/leo-joskowicz/" rel="nofollow" target="_blank">Leo Joskowicz</a> is a pioneer of Computer Assisted Intervention and the President of MICCAI society. He is a professor at the School of Engineering and Computer Science at the Hebrew University of Jerusalem. In this episode, we explore his interest in Geometry, Shape and making MICCAI society a home to all scientists working in medical imaging problems, no matter their geographical location.

Leo Joskowicz: Shaping up AI and MICCAI

Podcast is a great clinical culture for Knowledge dissemination, constructive arguments &amp; deep discussions. One more way in which the MICCAI community is different from those who we serve.Introducing "AI-ready Healthcare", my podcast to bridge this gap. I invite fellow researchers from both communities to talk about the translational aspects of AI research into clinical care. Often we converse with my co-host Henry Krumb.

Dan Stoyanov is a Professor of Robot Vision in the Department of Computer Science at University College London, Director of the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Royal Academy of Engineering Chair in Emerging Technologies and a Fellow of the Institution of Engineering and Technology. Dan is also Chief Scientific Officer at Digital Surgery Ltd and Co-Founder of Odin Medical, both companies specializing in developing AI products for interventional healthcare. You can follow Dan on Twitter @DanStoyanov.

<a href="https://hamlynsymposium.org/people/professor-dan-stoyanov/" rel="nofollow" target="_blank">Dan Stoyanov</a> is a Professor of Robot Vision in the Department of Computer Science at University College London, Director of the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Royal Academy of Engineering Chair in Emerging Technologies and a Fellow of the Institution of Engineering and Technology. Dan is also Chief Scientific Officer at Digital Surgery Ltd and Co-Founder of Odin Medical, both companies specializing in developing AI products for interventional healthcare. You can follow Dan on Twitter <a href="https://twitter.com/danstoyanov?lang=en" rel="nofollow" target="_blank">@DanStoyanov</a>.

Dan Stoyanov: Surgical Data Science 101

John Mongan is the Associate Chair for Translational Informatics, Director of the Center for Intelligent Imaging and an Associate Professor of Clinical Radiology (Abdominal Imaging and Ultrasound section) in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco. His research focuses on AI in medical imaging. In this session, we discussed the business case of Radiology AI and his Checklist for Artificial Intelligence in Medical Imaging (CLAIM).  You can find him in Twitter @MonganMD.

<a href="https://profiles.ucsf.edu/john.mongan" rel="nofollow" target="_blank">John Mongan</a> is the Associate Chair for Translational Informatics, Director of the Center for Intelligent Imaging and an Associate Professor of Clinical Radiology (Abdominal Imaging and Ultrasound section) in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco. His research focuses on AI in medical imaging. In this session, we discussed the business case of Radiology AI and his <a href="https://pubs.rsna.org/doi/10.1148/ryai.2020200029" rel="nofollow" target="_blank">Checklist for Artificial Intelligence in Medical Imaging (CLAIM)</a>.  You can find him in Twitter <a href="https://twitter.com/MonganMD" rel="nofollow" target="_blank">@MonganMD</a>.

John Mongan: To buy or not to buy radiology AI

Qi Dou is an Assistant Professor from The Chinese University of Hong Kong. Her research focus is on the interdisciplinary field of medical image analysis, artificial intelligence and robotics. In this episode, we talked about the importance of Federated Learning in medical imaging and in particular, her paper Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. You can find her in Twitter @QiDou_.

<a href="http://www.cse.cuhk.edu.hk/~qdou/" rel="nofollow" target="_blank">Qi Dou</a> is an Assistant Professor from The Chinese University of Hong Kong. Her research focus is on the interdisciplinary field of medical image analysis, artificial intelligence and robotics. In this episode, we talked about the importance of Federated Learning in medical imaging and in particular, her paper <a href="https://www.nature.com/articles/s41746-021-00431-6" rel="nofollow" target="_blank">Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study</a>. You can find her in Twitter <a href="https://twitter.com/QiDou_" rel="nofollow" target="_blank">@QiDou_</a>.

Qi Dou: Federated Learning for radiology

Professor Andreas Maier leads the Pattern Recognition Lab of Friedrich-Alexander-Universität of Erlangen-Nürnberg. In this episode, we discussed his research on Known Operator Learning and in particular his paper Learning with known operators reduces maximum error bounds. You can find him in Twitter @maier_ak.

Professor <a href="https://lme.tf.fau.de/person/maier/#collapse_0" rel="nofollow" target="_blank">Andreas Maier</a> leads the Pattern Recognition Lab of Friedrich-Alexander-Universität of Erlangen-Nürnberg. In this episode, we discussed his research on Known Operator Learning and in particular his paper <a href="https://www.nature.com/articles/s42256-019-0077-5" rel="nofollow" target="_blank">Learning with known operators reduces maximum error bounds</a>. You can find him in Twitter <a href="https://twitter.com/maier_ak" rel="nofollow" target="_blank">@maier_ak</a>.

Andreas Maier: Known operator learning for medical imaging

Professor Alex Frangi is Diamond Jubilee Chair in Computational Medicine and Royal Academy of Engineering Chair in Emerging Technologies at the University of Leeds, Leeds, UK, with joint appointments at the School of Computing and the School of Medicine. He directs the CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine. In this episode, we discuss computational medicine, the promises of in-silico trials and his new paper In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials.

Professor <a href="https://eps.leeds.ac.uk/computing/staff/1535/professor-alejandro-f-frangi" rel="nofollow" target="_blank">Alex Frangi</a> is Diamond Jubilee Chair in Computational Medicine and Royal Academy of Engineering Chair in Emerging Technologies at the University of Leeds, Leeds, UK, with joint appointments at the School of Computing and the School of Medicine. He directs the <a href="http://www.cistib.org/" rel="nofollow" target="_blank">CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine</a>. In this episode, we discuss computational medicine, the promises of in-silico trials and his new paper <a href="https://www.nature.com/articles/s41467-021-23998-w" rel="nofollow" target="_blank">In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials</a>.

Alex Frangi: Unlocking In-silico clinical trials

Dr. Yuri Tolkach, a pathologist and researcher from Uniklinik Köln, is breaking new grounds in digital pathology with probing questions about the usability of deep learning. In this episode, we discussed in great details his recent article on Quality control stress test for deep learning-based diagnostic model in digital pathology.

Dr. <a href="https://pathologie.uk-koeln.de/forschung/nachwuchsgruppen/uropathologie-ag-tolkach/" rel="nofollow" target="_blank">Yuri Tolkach</a>, a pathologist and researcher from Uniklinik Köln, is breaking new grounds in digital pathology with probing questions about the usability of deep learning. In this episode, we discussed in great details his recent article on <a href="https://www.nature.com/articles/s41379-021-00859-x" rel="nofollow" target="_blank">Quality control stress test for deep learning-based diagnostic model in digital pathology</a>.

Yuri Tolkach: Silent Failures of Deep Digital Pathology

Marius Linguraru

Marius Linguraru is a principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children's National Hospital in Washington, D.C. Dr. Linguraru is also professor of Radiology and Pediatrics and secondary professor of Biomedical Engineering at George Washington University. He co-founded PediaMetrix Inc., a company focused on infant well-being by creating solutions to improve the management of conditions of early childhood. Finally Dr. Linguraru is a board member of MICCAI society with a focus on early career development of young researchers. In this episode we discussed in depth about his research on pediatric health and his activities for the MICCAI society.  

<a href="https://childrensnational.org/research/people/marius-george-linguraru" rel="nofollow" target="_blank">Marius Linguraru</a> is a principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children's National Hospital in Washington, D.C. Dr. Linguraru is also professor of Radiology and Pediatrics and secondary professor of Biomedical Engineering at George Washington University. He co-founded <a href="https://www.pediametrix.com/" rel="nofollow" target="_blank">PediaMetrix Inc.</a>, a company focused on infant well-being by creating solutions to improve the management of conditions of early childhood. Finally Dr. Linguraru is a board member of MICCAI society with a focus on early career development of young researchers. In this episode we discussed in depth about his research on pediatric health and his activities for the MICCAI society.

Marius Linguraru: Making babies fitter - within MICCAI and beyond

Arijit Patra, a senior machine learning scientist from AstraZeneca, discusses how AI can significantly speed-up pre-clinical imaging. He also discussed his PhD thesis on continual learning for fetal ultrasound imaging.

<a href="https://www.linkedin.com/in/arijit-patra-92a18457/?originalSubdomain=uk" rel="nofollow" target="_blank">Arijit Patra</a>, a senior machine learning scientist from AstraZeneca, discusses how AI can significantly speed-up pre-clinical imaging. He also discussed his PhD thesis on continual learning for fetal ultrasound imaging.

Arijit Patra: Big-Pharmas need imaging AI, and they don't know it yet! 

Indranil Mallick, a practicing oncologist from India, asserts the necessity of AI in Radiation Oncology. His reflections of practicing Oncology through the two waves of the pandemic in India is a reminder, how varied the demands are of the AI-readiness of healthcare across the globe.  

<a href="https://www.linkedin.com/in/indranil-mallick-836a059/" rel="nofollow" target="_blank">Indranil Mallick</a>, a practicing oncologist from India, asserts the necessity of AI in Radiation Oncology. His reflections of practicing Oncology through the two waves of the pandemic in India is a reminder, how varied the demands are of the AI-readiness of healthcare across the globe.

Indranil Mallick: Why Indian healthcare needs AI post COVID? Reflections of a Radiation Oncologist

Michal Rosen-Zvi, director of IBM Research's healthcare informatics, talks about her perspective on the usefulness of radiology AI during the pandemic. In particular, we discussed her recently published article On the role of artificial intelligence in medical imaging of COVID-19. With multiple articles describing similar concerns, this is a timely episode about a very relevant topic. Further reading: 1. Hundreds of AI tools have been built to catch covid. None of them helped. 2. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

<a href="https://researcher.watson.ibm.com/researcher/view.php?person=il-ROSEN" rel="nofollow" target="_blank">Michal Rosen-Zvi</a>, director of IBM Research's healthcare informatics, talks about her perspective on the usefulness of radiology AI during the pandemic. In particular, we discussed her recently published article <a href="https://www.cell.com/patterns/pdf/S2666-3899(21)00095-7.pdf" rel="nofollow" target="_blank">On the role of artificial intelligence in medical imaging of COVID-19</a>. With multiple articles describing similar concerns, this is a timely episode about a very relevant topic. Further reading: 1. <a href="https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/" rel="nofollow" target="_blank">Hundreds of AI tools have been built to catch covid. None of them helped.</a> 2. <a href="https://www.nature.com/articles/s42256-021-00307-0?fbclid=IwAR0YrQBSPI1KYm7QS2AORwHwTmO8wmtj9G_-B8MT2pjxKOTJ3mWb9IWzSXE" rel="nofollow" target="_blank">Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans</a>

Michal Rosen-Zvi: Underwhelming maturity of Radiology AI in COVID-19 imaging

Podcast is a great clinical culture for Knowledge dissemination, constructive arguments &amp; deep discussions. One more way in which the MICCAI community is different from those who we serve. Introducing "AI-ready Healthcare", my podcast to bridge this gap. I invite fellow researchers from both communities to talk about the translational aspects of AI research into clinical care. Often we converse with my co-host Henry Krumb.

AI-ready Healthcare

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