IBIIS & AIMI Hybrid Seminar: Anthony Gatti, PhD & Liangqiong Qu, PhD @ Clark Center S360
IBIIS & AIMI Hybrid Seminar: Anthony Gatti, PhD & Liangqiong Qu, PhD
Dec 14 @ 1:00 pm – 2:00 pm Clark Center S360

In Person at the Clark Center S360 – Lunch will be provided!

Anthony Gatti, PhD
Postdoctoral Research Fellow
Department of Radiology
Wu Tsai Human Performance Alliance
Stanford University

Title: Towards Understanding Knee Health Using Automated MRI-Based Statistical Shape Models

Abstract: Knee injuries and pain are prevalent across all ages, with varying causes from “anterior knee pain” in runners to osteoarthritis-related pain. Osteoarthritis pain is a particular problem because structural outcomes assessed on medical images often disagree with symptoms. Most studies trying to understand knee health and pain use simple biomarkers such as mean cartilage thickness. My talk will present an automated pipeline for quantifying the whole knee using statistical shape modeling. I will present a conventional statistical shape model as well as a novel approach that uses generative neural implicit representations. Both modeling approaches allow unsupervised identification of salient anatomic features. I will demonstrate how these features can be used to predict existing radiographic outcomes, patient demographics, and knee pain.

Liangqiong Qu, PhD
Postdoctoral Research Fellow
Department of Biomedical Data Sciences
Stanford University

Title: Distributed Deep Learning in Medical Imaging

Abstract: Distributed deep learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data.
In this talk, we will first investigate the use distributed deep learning to build medical imaging classification models in a real-world collaborative setting.
We then present several strategies to tackle the data heterogeneity challenge and the lack of quality labeled data challenge in distributed deep learning.

IBIIS & AIMI Zoom Seminar: Biologically Inspired Deep Learning as a New Window into Brain Dysfunction @ Zoom:
IBIIS & AIMI Zoom Seminar: Biologically Inspired Deep Learning as a New Window into Brain Dysfunction
Jan 18 @ 12:00 pm – 1:00 pm Zoom:

Archana Venkataraman, PhD
Associate Professor of Electrical and Computer Engineering
Boston University

Title: Biologically Inspired Deep Learning as a New Window into Brain Dysfunction

Abstract: Deep learning has disrupted nearly every major field of study from computer vision to genomics. The unparalleled success of these models has, in many cases, been fueled by an explosion of data. Millions of labeled images, thousands of annotated ICU admissions, and hundreds of hours of transcribed speech are common standards in the literature. Clinical neuroscience is a notable holdout to this trend. It is a field of unavoidably small datasets, massive patient variability, and complex (largely unknown) phenomena. My lab tackles these challenges across a spectrum of projects, from answering foundational neuroscientific questions to translational applications of neuroimaging data to exploratory directions for probing neural circuitry. One of our key strategies is to integrate a priori information about the brain and biology into the model design.

This talk will highlight two ongoing projects that epitomize this strategy. First, I will showcase an end-to-end deep learning framework that fuses neuroimaging, genetic, and phenotypic data, while maintaining interpretability of the extracted biomarkers. We use a learnable dropout layer to extract a sparse subset of predictive imaging features and a biologically informed deep network architecture for whole-genome analysis. Specifically, the network uses hierarchical graph convolution that mimic the organization of a well-established gene ontology to track the convergence of genetic risk across biological pathways. Second, I will present a deep-generative hybrid model for epileptic seizure detection from scalp EEG. The latent variables in this model capture the spatiotemporal spread of a seizure; they are complemented by a nonparametric likelihood based on convolutional neural networks. I will also highlight our current end-to-end extensions of this work focused on seizure onset localization. Finally, I will conclude with exciting future directions for our work across the foundational, translational, and exploratory axes.

IBIIS & AIMI Seminar: Computational Pathology - Towards Precision Medicine @ Zoom:
IBIIS & AIMI Seminar: Computational Pathology – Towards Precision Medicine
Feb 15 @ 9:30 am – 10:30 am Zoom:

Andrew Janowczyk, PhD
Assistant Professor
Department of Biomedical Engineering
Emory University

Title: Computational Pathology: Towards Precision Medicine

Roughly 40% of the population will be diagnosed with some form of cancer in their lifetime. In a large majority of these cases, a definitive cancer diagnosis is only possible via histopathologic confirmation on a tissue slide. With the increasing popularity of the digitization of pathology slides, a wealth of new untapped data is now regularly being created.

Computational analysis of these routinely captured H&E slides is facilitating the creation of diagnostic tools for tasks such as disease identification and grading. Further, by identifying patterns of disease presentation across large cohorts of retrospectively analyzed patients, new insights for predicting prognosis and therapy response are possible [1,2]. Such biomarkers, derived from inexpensive histology slides, stand to improve the standard of care for all patient populations, especially where expensive genomic testing may not be readily available. Moreover, since numerous other diseases and disorders, such as oncoming clinical heart failure [3], are similarly diagnosed via pathology slides, those patients also stand to benefit from these same technological advances in the digital pathology space.

This talk will discuss our research aimed towards reaching the goal of precision medicine, wherein patients receive optimized treatment based on historical evidence. The talk discusses how the applications of deep learning in this domain are significantly improving the efficiency and robustness of these models [4]. Numerous challenges remain, though, especially in the context of quality control and annotation gathering. This talk further introduces the audience to open-source tools being developed and deployed to meet these pressing needs, including quality control ( [5]), annotation (, labeling (, validation (

IBIIS & AIMI Seminar: What Makes a 'Good' Decision? An Empirical Bioethics Study of Using AI at the Bedside @
IBIIS & AIMI Seminar: What Makes a ‘Good’ Decision? An Empirical Bioethics Study of Using AI at the Bedside
Mar 15 @ 12:00 pm – 1:00 pm

Melissa McCradden, PhD
John and Melinda Thompson Director of Artificial Intelligence in Medicine
Integration Lead, AI in Medicine Initiative
Bioethicist, The Hospital for Sick Children (SickKids)
Associate Scientist, Genetics & Genome Biology
Assistant Professor, Dalla Lana School of Public Health

Title: What Makes a ‘Good’ Decision? An Empirical Bioethics Study of Using AI at the Bedside

Abstract: This presentation will identify the gap between AI accuracy and making good clinical decisions. I will present a study where we develop an ethical framework for clinical decision-making that can help clinicians meet medicolegal and ethical standards when using AI that does not rely on explainability, nor perfect accuracy of the model.

IBIIS & AIMI Hybrid Seminar: Designing Machine Learning Processes For Equitable Health Systems @ ZOOM:
IBIIS & AIMI Hybrid Seminar: Designing Machine Learning Processes For Equitable Health Systems
Apr 19 @ 12:00 pm – 1:00 pm ZOOM:

Marzyeh Ghassemi, PhD
Assistant Professor, Department of Electrical Engineering and Computer Science
Institute for Medical Engineering & Science
Massachusetts Institute of Technology (MIT)
Canadian CIFAR AI Chair at Vector Institute

Title: Designing Machine Learning Processes For Equitable Health Systems

Dr. Marzyeh Ghassemi focuses on creating and applying machine learning to understand and improve health in ways that are robust, private and fair. Dr. Ghassemi will talk about her work trying to train models that do not learn biased rules or recommendations that harm minorities or minoritized populations. The Healthy ML group tackles the many novel technical opportunities for machine learning in health, and works to make important progress with careful application to this domain.