The conference where information is shared by leading researchers in image processing, physics, computer-aided diagnosis, perception, image-guided procedures, biomedical applications, ultrasound, informatics, radiology, digital pathology, and much more.
Cynthia Rudin, head of the Interpretable Machine Learning Lab at Duke University, discusses applying interpretable neural networks to analyze mammograms and EEG signals in her talk at SPIE Medical Imaging. She will further explain how interpretable machine learning can provide radiologists with actual medical insight.
Lena Maier-Hein of the German Cancer Research Center (DKFZ) urges AI researchers to “question everything!” In her presentation she shares, "If we really want to generate patient impact, we need solutions that work in the wild. To this end, we need to design every step of an image analysis pipeline very carefully, from the choice of input data to the annotations and the validation strategy."
Curtis Langlotz of Stanford University speaks to machine learning in medical practice. He gives the audience a sense of the future of AI in medical imaging, the key AI research themes, the paths to clinical impact, the role of foundation models, and some of the pitfalls to watch for.
Frank Rybicki of the University of Arizona discusses 3D printing in radiology. The ultimate goal for Health Care Facilities (HCFs), is bringing care close to the patient and devices closer to the surgeon. The use of 3D imaging offers that ability.
The Medical Imaging community is about sharing important research and the latest advancements to help move research and technology into the future. This meeting supports leading researchers doing important work. Check out what's been happening onsite.