Visual AI in Healthcare
Sept 19, 2024 at 8:30 AM Pacific / 11:30 AM Eastern
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Interpretable AI Models in Radiology
Dr. Tolga Tasdizen
University of Utah
AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.
About the Speaker
Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.
Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology
Dr. Christopher Pinard, DVM DVSc DACVIM
ANI.ML Health and Ontario Veterinary College, University of Guelph
Dr. Kuan-Chuen Wu
ANI.ML Health
Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision – making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine.
About the Speakers
Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications.
Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good.
Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models
Daniel Gural
Voxel51
In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks.
About the Speaker
Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.
Exploring Instance Imbalance in Medical Semantic Segmentation
Soumya Snigdha Kundu
Kings College London
Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation.
About the Speaker
Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.
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