Aug 20 - In-Person - Raleigh AI, ML and Computer Vision Meetup

Join us for an evening of talks from experts on AI, ML, and Computer Vision co-presented by Voxel51 and Teksystems.

Date and Time

• Aug 20, 2025
• 5:30 PM - 8:30 PM

Location

TEKsystems
4300 Edwards Mill Rd
Raleigh, NC

Adapting Vision Foundation Models to Medical Imaging: Strategies and Clinical Applications

Foundation models like SAM and DINO-v2 have shown strong performance on natural image tasks. However, when applied directly to medical imaging, they often underperform due to domain shifts, limited labeled data, and modality-specific challenges. This raises an important question: how can we adapt foundation models to work reliably and meaningfully in medical images?

In this talk, I will share our research efforts toward answering that question. I will begin by exploring several fine-tuning strategies for different data scenarios, ranging from few-shot labeled examples to large collections of unlabeled scans. These strategies aim to help identify the optimal adaptation framework under various data availability settings. I will then introduce a series of models we developed based on these insights. SegmentAnyBone and SegmentAnyMuscle are two SAM-based models designed for accurate bone and muscle segmentation across all body locations and a wide range of MRI sequences. MRI-Core is a self-supervised model that learns general-purpose MRI features from unlabeled data and can be easily adapted to multiple downstream tasks.

Finally, I will present a clinical application where one of these models is used to support abdominal surgical risk prediction. This example shows how I have explored using these models to contribute to real-world clinical decision-making. I hope this talk can share some of my experiences in building foundation models that are both practical for research and adaptable to clinical settings and to spark new insights and discussions in this field!

About the Speaker

Hanxue Gu is a 5th year Ph.D. student in Electrical and Computer Engineering at Duke University, working at the intersection of AI and Healthcare. I am fortunate to be advised by Prof. Maciej A. Mazurowski under Duke Spark Initiative. My research sits at the intersection of machine learning and healthcare, with a focus on developing and adapting deep learning methods for medical image analysis—from application-oriented tools to foundational advancements.

Bias & Batch Effects in Medical Imaging

Medical AI models can exhibit concerning biases, such as the ability to predict race from radiology images, which is impossible for human experts. This talk will examine bias and batch effects in medical imaging, beginning with a histopathology case study to illustrate the origins of some of these biases. I'll cover detection methods, such as exploratory data analysis, and mitigation strategies, including careful cross-validation and model-level interventions. While research has shown that foundation models reduce some biases, they don't eliminate the problem entirely. Bias represents a fundamental challenge in medical AI requiring early detection, careful validation, and tailored mitigation approaches.

About the Speaker

Heather D. Couture is a consultant and founder of Pixel Scientia Labs, where she partners with mission-driven founders and R&D teams to support applications of computer vision for people and planetary health. She has a PhD in Computer Science and has published in top-tier computer vision and medical imaging venues. She hosts the Impact AI Podcast and writes regularly on LinkedIn, for her newsletter Computer Vision Insights, and for a variety of other publications.

Managing Medical Imaging Datasets: From Curation to Evaluation

High-quality data is the cornerstone of effective machine learning in healthcare. This talk presents practical strategies and emerging techniques for managing medical imaging datasets, from synthetic data generation and curation to evaluation and deployment.

We’ll begin by highlighting real-world case studies from leading researchers and practitioners who are reshaping medical imaging workflows through data-centric practices. The session will then transition into a hands-on tutorial using FiftyOne, the open-source platform for visual dataset inspection and model evaluation. Attendees will learn how to load, visualize, curate, and evaluate medical datasets across various imaging modalities.

Whether you're a researcher, clinician, or ML engineer, this talk will equip you with practical tools and insights to improve dataset quality, model reliability, and clinical impact.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.

Learning with Small Datasets in Real-World Medical Imaging Applications

The talk will explore approaches that are effective when developing computer vision models for real-world medical imaging applications in situations where available datasets are limited in size. This setting is of special interest because the most challenging prediction problems in the medical domain have usually low incidence rates, thus resulting in (relatively) small datasets. Specifically, it will consider data-efficient architectures, multi-task learning and data augmentation through pseudo interventions. For illustration, a use case in which volumetric ophthalmology images are used to predict geographic atrophy conversion will be discussed.

About the Speaker

Ricardo Henao, a quantitative scientist, is an Associate Professor in the department of Biostatistics and Bioinformatics, Department of Electrical and Computer Engineering (ECE), Surgery, member of the Information Initiative at Duke (iiD), Duke AI Health and the Duke Clinical Research Institute (DCRI), all at Duke University. He also serves as the Associate Director of Clinical Trials AI at DCRI. His recent work has been focused on the development of machine learning models, predominantly deep learning and representation learning approaches, for the analysis and interpretation of clinical and biological data with applications to predictive modeling for diverse clinical outcomes.