Welcome to the Summer School - Presentation of MICCAI and RISE

presented by Esther Puyol Esther Puyol
Esther Puyol holds a Bachelor's and Master's in Biomedical Signal Processing from the Polytechnic University of Catalonia and a dual Master's in Engineering and Medical Imaging from Telecom Bretagne, all completed in 2014. She earned her PhD in Biomedical Engineering at King's College London (2014–2018), where she developed a multimodal statistical atlas of heart function using cardiac MR and ultrasound imaging. Afterward, she worked as a research fellow in automated CMR analysis and fairness. Esther is currently a Research Scientist at HeartFlow and a Visiting Lecturer at King's College London. Her research interests include AI applied to cardiac imaging and fairness. , Marius George Linguraru Marius George Linguraru
Marius George Linguraru, DPhil, MA, MSc loves to work with multidisciplinary teams of clinicians, scientists, and engineers to advance global health and improve the lives of vulnerable children and patients with rare diseases through AI-driven solutions. He is the Connor Family Professor and Endowed Chair in Research and Innovation at Children's National Hospital in Washington, D.C., where he leads the AI research initiatives. He also holds faculty appointments as Professor of Radiology and Pediatrics at The George Washington University. Dr. Linguraru is the President of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society.

New trends in AI

presented by Qingyu Zhao Qingyu Zhao
Assistant Professor of Artificial Intelligence in Radiology

Good practices in AI

presented by Lena Maier-Hein Lena Maier-Hein
Lena Maier-Hein is head of the division Intelligent Medical Systems at the German Cancer Research Center (DKFZ) and serves as managing director of the DKFZ Data Science and Digital Oncology cross-topic program. Her research concentrates on machine learning-based biomedical image analysis with a specific focus on surgical data science, computational biophotonics and validation of machine learning algorithms.

Challenge and Mentorship Programme

presented by Natasha Lepore Natasha Lepore
I am an Associate Professor of Radiology and Biomedical Engineering at the University of Southern California and Children's Hospital Los Angeles. My laboratory, the Computational Imaging of Brain Organization Research Group (CIBORG), specializes in advanced computational methods to study brain anatomy and function using magnetic resonance imaging (MRI). Our work aims to improve our understanding of neurological disorders, as well as normal and abnormal brain development, in both high- and low-resource settings. , Antonio Porras Perez Antonio Porras Perez
Dr. Antonio R. Porras is an Associate Professor with Tenure at the University of Colorado Anschutz Medical Campus, and founder of the Medical Imaging and Machine Intelligence initiative. He also directs the research program of Pediatric Plastic and Reconstructive Surgery at Children's Hospital Colorado. Dr. Porras is an expert in computer science, biomedical engineering, medical image computing and machine learning with diverse medical applications and domains including radiology, cardiology, surgery, pediatrics and medical genetics.

Design Thinking in AI

presented by Islem Rekik Islem Rekik
Islem Rekik is the Director of the Brain And SIgnal Research and Analysis (BASIRA) laboratory and an Associate Professor at Imperial College London (Innovation Hub I-X). Together with BASIRA members, she conducted more than 90 cutting-edge research projects cross-pollinating AI and healthcare —with a sharp focus on brain imaging and neuroscience. She is also a co/chair/organizer of more than 20 international first-class conferences/workshops/competitions. Dr Rekik has been awarded prestigious international research fellowships including the EU Marie-Curie Fellowship in 2019 and the TUBITAK 2232 for Outstanding Experienced Researchers during 2020-2022. In addition to her 130+ high-impact publications, she is a strong advocate of equity, inclusiveness and diversity in research. She is the former president of the Women in MICCAI (WiM), the co-founder of the international RISE Network to Reinforce Inclusiveness & diverSity and Empower minority researchers in Low-Middle Income Countries (LMIC) and a committee member of the AFRICAI network.

Diffusion Models For Medical Imaging: Lecture

presented by Jorge Cardoso Jorge Cardoso
M Jorge Cardoso is Reader in Artificial Medical Intelligence at King's College London, where he leads a research portfolio on big data analytics, quantitative radiology and value-based healthcare. Jorge is also the CTO of the new London Medical Imaging and AI Centre for Value-based Healthcare. He has more than 15 years expertise in advanced image analysis, big data, and artificial intelligence, and co-leads the development of project MONAI, a deep-learning platform for artificial intelligence in medical imaging.

Diffusion Models For Medical Imaging: Hands on

presented by Jorge Cardoso Jorge Cardoso
M Jorge Cardoso is Reader in Artificial Medical Intelligence at King's College London, where he leads a research portfolio on big data analytics, quantitative radiology and value-based healthcare. Jorge is also the CTO of the new London Medical Imaging and AI Centre for Value-based Healthcare. He has more than 15 years expertise in advanced image analysis, big data, and artificial intelligence, and co-leads the development of project MONAI, a deep-learning platform for artificial intelligence in medical imaging.

MICCAI Workshop presentation: iMIMIC, MIRASOL and STACOM

presented by Mauricio Reyes (iMIMIC) Mauricio Reyes
Professor Mauricio Reyes began his academic journey in 2003 when he was awarded a French-Chilean scholarship to conduct his PhD studies at INRIA, France. His research work focused on developing AI technologies to improve the workflow for cancer patients. , Udunna Anazodo (MIRASOL) Udunna Anazodo
Udunna Anazodo, PhD, Assistant Professor in the Department of Neurology and Neurosurgery, is also a member of the Neuroimaging and Neuroinformatics research group at The Neuro. She joined The Neuro from the Lawson Health Research Institute in London, Ontario where she was Assistant Professor of Medical Biophysics. , Alistair Young (STACOM) Alistair Young
Alistair Young is Professor of Cardiovascular Data Analytics and AI at King's College, London. His research interests are in machine learning, biomechanical engineering and statistical modelling of heart function in cardiovascular disease. He has extensive experience working with industry to translate technologies into clinical products. He has published widely with over 190 journal publications. He is principal investigator of the Cardiac Atlas Project which seeks to provide cardiac imaging data and analysis tools to the research community for quantification of heart shape and function changes in large cohort studies (www.cardiacatlas.org). As part of the Medical Image Computing and Computer Assisted Intervention Society he has co-edited 15 volumes of proceedings of the Statistical Atlases and Computational Models of the Heart (STACOM) workshop, which facilitates open benchmarking and evaluation of computational algorithms for improving analysis and understanding of heart disease.

presented by Randy Olson, Matthew David, Elizabeth Strauss (ABT Narrative Team)

presented by Charles Delahunt Charles Delahunt
Charles Delahunt is a Senior Research Engineer at Global Health Labs in Bellevue, Washington. He has 13 years' experience applying ML to global health challenges including malaria diagnosis, neglected tropical disease diagnosis, ultrasound, and intrapartum monitoring. He also held a postdoc researching ML methods at University of Washington's applied math department. He has served as an Area Chair for MICCAI and ML4H, and is an Academic Editor for PLOS Digital Health.

MICCAI review

presented by Andreas Maier Andreas Maier
Prof. Dr. Andreas Maier was born on 26th of November 1980 in Erlangen. He studied Computer Science, graduated in 2005, and received his PhD in 2009. From 2005 to 2009 he was working at the Pattern Recognition Lab at the Computer Science Department of the University of Erlangen-Nuremberg. His major research subject was medical signal processing in speech data. From 2009 to 2010, he started working on flat-panel C-arm CT as post-doctoral fellow at the Radiological Sciences Laboratory in the Department of Radiology at the Stanford University. From 2011 to 2012 he joined Siemens Healthcare as innovation project manager and was responsible for reconstruction topics in the Angiography and X-ray business unit. In 2012, he returned the University of Erlangen-Nuremberg as head of the Medical Reconstruction Group at the Pattern Recognition lab. In 2015 he became professor and head of the Pattern Recognition Lab. Current research interests focuses on medical imaging, image and audio processing, digital humanities, and interpretable machine learning and the use of known operators.

Past MICCAIs, Future Insights: An Attendee Retrospective

presented by Nahal Mirzaie Nahal Mirzaie
Nahal Mirzaie is a PhD candidate in Artificial Intelligence at Sharif University of Technology, Iran, advised by Dr. M.H. Rohban at the Robust and Interpretable Machine Learning (RIML) lab. Her research focuses on robustness in machine learning, with particular interest in group robustness, spurious correlations, and shortcut learning. She is also broadly interested in trustworthy AI and its applications in fields such as computational pathology and drug discovery. , Ajibola Oladokun Ajibola Oladokun
Ajibola Samson Oladokun is a PhD candidate in Biomedical Engineering at the University of Cape Town, South Africa. He holds an MSc in Microprocessor and Control Engineering from the University of Ibadan, Nigeria, and a B.Eng. in Electronics and Electrical Engineering from Osun State University, Nigeria. He was recognized as an Outstanding Reviewer for MICCAI 2024 and his paper "SpeChrOmics" was accepted for oral presentation at the conference. He currently works as a Research and Development Engineer at IMT Atlantique. His research interests include Hyperspectral Imaging, Medical Image Processing, Machine Learning, Optical Devices, Embedded Systems, Software Development, and Wireless Sensor Networks. , Maruf Adewole Maruf Adewole
Maruf Adewole, PhD, is a Medical Physicist and a Postdoctoral Research Fellow at the University of Pennsylvania. He doubles as the Executive Director of the Medical Artificial Intelligence Laboratory (MAI Lab) based in Lagos, Nigeria. His work focuses on advancing AI-driven medical imaging solutions for cancer diagnosis in resource-constrained settings. Through initiatives like BraTS-Africa, ABreast, and SPARK Academy, he promotes global health equity by curating inclusive datasets, developing ML models, and building local AI capacity.

presented by Angelica I Aviles-Rivero Angelica I Aviles-Rivero
Angelica Aviles-Rivero is an Assistant Professor at the Yau Mathematical Sciences Center, Tsinghua University. Previously, she was a Senior Research Associate at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She is a member of ELLIS. Her research lies at the intersection of applied mathematics and machine learning, focusing on developing data-driven algorithmic techniques that enable computers to extract high-level understanding from vast datasets.

Graph Learning in Medical Image Analysis: Lecture + Hands on

presented by Angelica I Aviles-Rivero Angelica I Aviles-Rivero
Angelica Aviles-Rivero is an Assistant Professor at the Yau Mathematical Sciences Center, Tsinghua University. Previously, she was a Senior Research Associate at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She is a member of ELLIS. Her research lies at the intersection of applied mathematics and machine learning, focusing on developing data-driven algorithmic techniques that enable computers to extract high-level understanding from vast datasets.

presented by Maxime di Folco Maxime di Folco
Maxime Di Folco is a Post-doctoral researcher at the Institute of Machine Learning for Biomedical Imaging at Helmholtz Munich. His research focuses on representation learning methods that aim to acquire low-dimensional representations of high-dimensional data, with a strong emphasis on cardiac imaging applications and multimodal integration. , Marta Maria Hasny Marta Maria Hasny
Marta Hasny is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and the Technical University of Munich (TUM). She received her B.Sc. in Computer Science from Pace University and completed her M.Sc. in Biomedical Computing at TUM. For her master's thesis at Harvard Medical School, she worked on improving the visualization of myocardial scar in late gadolinium enhancement cardiac MR using diffusion models. Her research interests include generative AI, foundation models, and their applications in cardiology.

MICCAI Workshop presentation: FAIMI, EMERGE and Agentic AI for Medicine

presented by Andrew King (FAIMI) Andrew King
Andrew King is a Professor in Medical Image Analysis in the School of Biomedical Engineering and Imaging Sciences at King’s College London (KCL). Dr King received a PhD degree in Computer Science from Warwick University in 1997 under the supervision of Professor Roland Wilson. From 2001-2005 he worked as an Assistant Professor in the Computer Science department at Mekelle University in Northern Ethiopia. Since 2006 he has worked at King’s College London, focusing on image analysis and AI in medical imaging. His research focuses on a range of methods and applications but he has a particular interest in trustworthy AI and algorithmic fairness for medical image analysis. , Naren Akash (EMERGE) Naren Akash
Naren Akash is presenting on the EMERGE workshop at MICCAI. , Baoru Huang (Agentic AI) Baoru Huang
Dr. Baoru Huang is an Assistant Professor at the University of Liverpool, specializing in Medical AI and Robotics. She earned her PhD from Imperial College London funded by NIHR, and previously worked as a Research Fellow at University College London, funded by EU Horizon Programme. She was also a Research Scientist at Meta Reality Labs. Dr. Huang has co-chaired the Hamlyn Winter School in 2022 and served as a lead organizer for Medical Robotics Workshops at the Hamlyn Symposium in 2022 and 2023. In October 2023, her oral presentation at the MICCAI conference in Vancouver was featured in ‘MICCAI 2023 Daily’, and earlier that year, she was awarded ‘DAAD AInet fellowship’ from the German Academic Exchange Service. Her research has been recognized with multiple awards, including the ‘Best Paper Award’ at the 11th International Conference on Robot Intelligence Technology and Applications (RiTA) in 2023 and the ‘Best Poster Award’ at the 17th European Molecular Imaging Meeting (ESMI) in 2022. She has over 30 publications in leading conferences and journals, including ICRA, IROS, RA-L, CVPR, ECCV, NeurIPS, MICCAI, and MedIA. Beyond her research, Dr. Huang is an active contributor to the academic community, serving as a reviewer for top-tier conferences and journals and in 2024, she served as an Area Chair for MICCAI and IPCAI.

presented by Eric Kerfoot Eric Kerfoot
Eric is the software architect for the Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences. He has been involved in software development at KCL for internal projects and global collaborations such as Project MONAI, as well as biomedical imaging research. He completed in DPhil in software engineering in 2010 at the University of Oxford specialising in formal methods and programming languages.

Uncertainty Quantification in Medical Image Analysis: Lecture

presented by Carole Sudre Carole Sudre
Dr.Carole Sudre is a biomedical engineer with a background in applied mathematics, computer science and biology with a special interest in medical image analysis and biomarker extraction. Carole Sudre works at the interface between population science and image processing methodological developments.

Uncertainty Quantification in Medical Image Analysis: Hands on

presented by Carole Sudre Carole Sudre
Dr.Carole Sudre is a biomedical engineer with a background in applied mathematics, computer science and biology with a special interest in medical image analysis and biomarker extraction. Carole Sudre works at the interface between population science and image processing methodological developments.

Inspirational talk & Closing

presented by Udunna Anazodo Udunna Anazodo
Udunna Anazodo, PhD, Assistant Professor in the Department of Neurology and Neurosurgery, is also a member of the Neuroimaging and Neuroinformatics research group at The Neuro. Udunna's research at The Neuro focuses on intelligent PET imaging for mapping brain health: from molecules to mind. Her lab combines positron emission tomography (PET) and magnetic resonance imaging (MRI) techniques to provide a multimodal imaging approach for detecting subtle and early changes in brain function, physiology and neurochemistry.