A wide variety of medical imaging tools (CT,MRI, pathology etc.) are used in routine medical care, and represent robust datasets for potential biomedical discovery using Artificial Intelligence (AI).A huge challenge is that each of these imaging tools produce highly complex images, and algorithms developed for one imaging modality typically are inaccurate when applied to another imaging type. With recent break throughs, researchers at Providence, Microsoft and the University of Washington have published a new all-in-one tool called BiomedParse that jointly conducts segmentation, detection, and feature recognition across nine different medical imaging modalities. This work paves the way for cutting-edge multimodal biomedical discovery, e.g., research that can be performed across large datasets regardless of imaging type.
