Invited Speakers of MICAD 2024

(Alphabetize by Last Name)

 

Assoc. Prof. Oliver Faust

Anglia Ruskin University (ARU), UK

Assoc. Prof. Oliver Faust, Anglia Ruskin University (ARU) Oliver has worked at the forefront of artificial intelligence in biomedical engineering. He is an international expert for medical decision support systems, artificial intelligence, the Internet of Medical Things, and signal processing, substantiated by his considerable publication record which includes 100 impact factor journal papers. He is a founding member of the Transformative Artificial Intelligence Applications research cluster, and a member of the Computing, Informatics and Applications Research Group at ARU.
Oliver holds two doctoral degrees (Doctor of Philosophy in electronic engineering from the University of Aberdeen; Doctor of Engineering in biomedical science from Chiba University, Japan). He's a founding faculty member of Habib University in Karachi, Pakistan, and assisted the startup of Altreonic Inc, a software company in Belgium. He collaborates with more than 20 distinguished biomedical scientists worldwide. Previously, he was a visiting scholar at Tianjin University, Zhejiang University, and University of Electronic Science and Technology of China.
Speech title: Artificial intelligence to analyse celiac pathology

Assoc. Prof. Tammy Riklin Raviv

Ben-Gurion University, Israel

Prof. Tammy Riklin Raviv leads the Biomedical Image Computing lab. at the School of Electrical and Computer Engineering of Ben-Gurion University (BGU). Her lab. develops deep learning and computer vision algorithms for the analysis of medical and microscopy imaging data and computational neuroscience.
She is a TC member at the IEEE Bio Imaging and Signal Processing (BISP) Committee, a handling editor in Neuroimage, and an associate editor at the IEEE Transactions on Medical Imaging journal.
She holds a B.Sc. in Physics and an M.Sc. in Computer Science both from the Hebrew University in Jerusalem, and a PhD from the School of Electrical Engineering of Tel-Aviv University. Prior to establishing her own research group at BGU (2012) she was a research fellow and a post-doctorate associate at the Computer Science and Artificial Intelligence lab. (CSAIL), MIT, at Harvard Medical School, and at the Broad Institute of MIT and Harvard.

Speech Title: HyperFusion: Imaging-Tabular Data Integration for Predictive Modeling in Healthcare
Abstract: The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. The integrative analysis of multiple sources can provide a comprehensive understanding of a patient's condition and can enhance diagnoses and treatment decisions. Deep Neural Networks (DNNs) consistently showcase outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. In the talk I will present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. The strength and the generality of our method is demonstrated on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex, and multi-class Alzheimer's Disease (AD) classification conditioned by tabular data. The proposed hyperfusion model is shown to outperform both single-modality models and state-of-the-art MRI-tabular data fusion methods.

Asst. Prof. Yanmei Tie

Harvard Medical School, USA

Dr. Tie is an Assistant Professor of Neurosurgery at Brigham and Women's Hospital, Harvard Medical School. Dr. Tie received her Ph.D. in Biomedical Engineering and M.S. in Electrical Engineering from Louisiana Tech University. Dr. Tie's research focuses on improving brain mapping for surgical planning and guidance by developing multi-modal neuroimaging techniques, focusing on functional magnetic resonance imaging fMRI and diffusion MRI. She has developed easy to perform resting state and movie watching fMRI paradigms for the patients who cannot perform regular task based fMRI due to language or other neurological deficits. Dr. Tie is also interested in applying brain connectivity to understand the neural mechanism of disease pathophysiology and develop biomarkers for neurological disorders. The goals are to inform therapeutic development and improve patients’ overall well-being.

Speech Title: Clinical Application of fMRI in Neurosurgical Brain Mapping
Abstract: Functional MRI (fMRI) has been widely applied as a neurosurgery planning tool for mapping of critical brain functions such as motor and language when brain lesions are close or within these functional areas. In this talk, I will first introduce neurosurgical brain mapping and the application of fMRI for surgical planning at our institution for the past 20 years. Then I will focus on my research which is to develop easy-to-perform fMRI paradigms, such as resting-state and movie-watching fMRI, to improve presurgical language mapping. The goal is to make fMRI surgical planning available to more patients, particularly those who are unable to perform traditional task-based fMRI due to language or other neurological conditions. Finally, I will talk about our on-going development of a movie clip database for naturalistic fMRI that can be applied to extend brain mapping to include emotion and other cognitive functions, in an effort to preserve and enhance patients’ overall well-being.

Prof. Yu-Chien Wu

Indiana University, USA

Dr. Yu-Chien Wu is a Professor at the Department of Radiology and Imaging Sciences with the academic area of excellence in research. She has complementary backgrounds in physics (BS, 1994) and medicine (MD, 2000). Integrating her knowledge in both disciplines, she earned her PhD in Medical Physics at the University of Wisconsin-Madison in 2006. She joined the Indiana University School of Medicine (IUSM) in 2013 first as a tenure-track Assistant Professor, later as a tenured Associate Professor in the Department of Radiology and Imaging Sciences (2019 - 2024), and recently as a full Professor. At IUSM, she pursues scholarly work, mentors young scientists, collaborates on and supports the development of research projects, and promotes state-of-the-art imaging technologies. Her research program focuses on the development of innovative neuroimaging technologies and their applications to elucidating disease mechanisms, facilitating early diagnoses, and identifying optimal treatments. She is a NIH-funded Principal Investigator of the NIA- and NINDS-funded projects (R01s, R21, and supplement) to detect early alterations in living human brains with neurological disorders, including Alzheimer’s disease (AD), mild traumatic brain injury (mTBI), and sport-related concussion (SRC).

Speech Title: Specific Analysis Approach to Characterize Heterogeneous White Matter Abnormalities in Sport-Related Concussion
Abstract: While sport-related concussion (SRC) is a mild form of traumatic brain injury, it may result in acute as well as long-lasting consequences for the brain [1-4]. MRI has been used to detect post-SRC neuropathophysiological progression noninvasively. Diffusion tensor imaging (DTI), one of the MRI techniques for detecting white-matter microarchitectures, has demonstrated the ability to identify acute changes of the brain post-SRC [5, 6] as well as persistent white matter alterations up to six months post-injury [7]. These microstructural changes have shown associations with clinical symptoms [8], recovery times [1], and blood biomarkers [9]. Nevertheless, these groupwise analyses require concurrent pathophysiological changes of the brain at the same anatomical locations to reach statistical significance and fail to account for cross-subject heterogeneity. SRC injury arises from unique biomechanical forces, with a highly-individualized potential impact [10]. Therefore, the heterogeneity in SRC white matter injury is best characterized by a subject-specific analysis that accounts for inter-subject variation [11-13]. In this work, we developed a subject-specific analysis approach to investigate longitudinal alterations in white matter after SRC.