June 27 - Visual AI in Healthcare

Join us for the third of several virtual events focused on the latest research, datasets and models at the intersection of visual AI and healthcare.

When

June 27 at 9 AM Pacific

Where

Online. Register for the Zoom!

MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders

We present MedVAE, a family of six generalizable 2D and 3D variational autoencoders trained on over one million images from 19 open-source medical imaging datasets using a novel two-stage training strategy. MedVAE downsizes high-dimensional medical images into compact latent representations, reducing storage by up to 512× and accelerating downstream tasks by up to 70× while preserving clinically relevant features. We demonstrate across 20 evaluation tasks that these latent representations can replace high-resolution images in computer-aided diagnosis pipelines without compromising performance. MedVAE is open-source with a streamlined finetuning pipeline and inference engine, enabling scalable model development in resource-constrained medical imaging settings.

About the Speakers

Ashwin Kumar is a PhD Candidate in Biomedical Physics at Stanford University, advised by Akshay Chaudhari and Greg Zaharchuk. He focuses on developing deep learning methodologies to advance medical image acquisition and analysis.

Maya Varma is a PhD student in computer science at Stanford University. Her research focuses on the development of artificial intelligence methods for addressing healthcare challenges, with a particular focus on medical imaging applications.

Leveraging Foundation Models for Pathology: Progress and Pitfalls

How do you train ML models on pathology slides that are thousands of times larger than standard images? Foundation models offer a breakthrough approach to these gigapixel-scale challenges. This talk explores how self-supervised foundation models trained on broad histopathology datasets are transforming computational pathology. We’ll examine their progress in handling weakly-supervised learning, managing tissue preparation variations, and enabling rapid prototyping with minimal labeled examples. However, significant challenges remain: increasing computational demands, the potential for bias, and questions about generalizability across diverse populations. This talk will offer a balanced perspective to help separate foundation model hype from genuine clinical value.

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.

LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging

Recent advances in promptable segmentation have transformed medical imaging workflows, yet most existing models are constrained to static 2D or 3D applications. This talk presents LesionLocator, the first end-to-end framework for universal 4D lesion segmentation and tracking using dense spatial prompts. The system enables zero-shot tumor analysis across whole-body 3D scans and multiple timepoints, propagating a single user prompt through longitudinal follow-ups to segment and track lesion progression. Trained on over 23,000 annotated scans and supplemented with a synthetic time-series dataset, LesionLocator achieves human-level performance in segmentation and outperforms state-of-the-art baselines in longitudinal tracking tasks. The presentation also highlights advances in 3D interactive segmentation, including our open-set tool nnInteractive, showing how spatial prompting can scale from user-guided interaction to clinical-grade automation.

About the Speaker

Maximilian Rokussis is a PhD scholar at the German Cancer Research Center (DKFZ), working in the Division of Medical Image Computing under Klaus Maier-Hein. He focuses on 3D multimodal and multi-timepoint segmentation with spatial and text prompts. With several MICCAI challenge wins and first-author publications at CVPR and MICCAI, he co-leads the Helmholtz Medical Foundation Model initiative and develops AI solutions at the interface of research and clinical radiology.

LLMs for Smarter Diagnosis: Unlocking the Future of AI in Healthcare

Large Language Models are rapidly transforming the healthcare landscape. In this talk, I will explore how LLMs like GPT-4 and DeepSeek-R1 are being used to support disease diagnosis, predict chronic conditions, and assist medical professionals without relying on sensitive patient data. Drawing from my published research and real-world applications, I’ll discuss the technical challenges, ethical considerations, and the future potential of integrating LLMs in clinical settings. The talk will offer valuable insights for developers, researchers, and healthcare innovators interested in applying AI responsibly and effectively.

About the Speaker

Gaurav K Gupta graduated from Youngstown State University, Bachelor’s in Computer Science and Mathematics.