A new field known as imageomics is on the horizon, promising to revolutionize the use of machine learning and computer vision in exploring the biology of organisms on a global scale.
Imageomics aims to combine images of living organisms with advanced computer analysis to delve into fundamental questions about biological processes on Earth. Wei-Lun Chao, an investigator at The Ohio State University’s Imageomics Institute, recently presented the latest research advancements in this field at the annual meeting of the American Association for the Advancement of Science.
Chao highlighted how imageomics has the potential to transform our understanding of the biological world by turning complex research questions into computable problems. With the help of machine learning and computer vision techniques, researchers believe they can significantly enhance the rate and efficiency of scientific discoveries.
By incorporating biological knowledge into machine learning models, imageomics researchers hope to improve interpretability and enhance scientific discovery capabilities. Chao and his colleagues are developing foundation models in imageomics to enable various tasks and create algorithms that can identify and discover traits in images without the need for extensive human annotation.
One of the key aspects of Chao’s research is teaching algorithms to actively search for specific traits in images, leading to more detailed and accurate visual analysis. This approach has shown promise in recognizing fine-grained species, such as butterfly mimicries, based on intricate patterns and coloring.
Chao envisions imageomics being applied across diverse fields, from biology to material science, thanks to its ease of use and potential for integration with other research areas. Collaboration among scientists from different disciplines is crucial for advancing imageomics research and harnessing its full potential.
Chao is optimistic about the future of imageomics and its ability to shed new light on the natural world through interdisciplinary collaboration. He believes that integrating AI with scientific knowledge is essential for the field’s continued growth and success.
Chao’s presentation at the AAAS meeting, titled “An Imageomics Perspective of Machine Learning and Computer Vision: Micro to Global,” was part of a session focused on the role of imageomics in understanding biological traits through machine learning.
