Imageomics: Discovering Biological Knowledge from Images Using AI

Imageomics is an emerging interdisciplinary field at the crossroads of machine learning (ML), computer vision (CV), and biological sciences. It leverages visual data—from microscopic images of single-cell species to videos of megafauna—to extract and analyze biological information, specifically traits. By grounding ML models in existing scientific knowledge, Imageomics aims to make traits computable from images, facilitating insights into the evolution and function of living organisms. Imageomics poses research problems that resonate with the broad machine-learning community: multimodal representation learning, object detection and tracking, few-shot learning, imbalanced-class learning, video understanding, 3D modeling, hierarchical learning, etc. When people leverage ML tools to solve biological questions, the foundational bridges between ML and biological sciences also provide opportunities to address key challenges in ML, creating a virtuous cycle between the two fields.