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New AI system could accelerate clinical research | By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
MIT researchers developed an interactive, AI-based system that enables users to rapidly annotate areas of interest in new biomedical imaging datasets, without training a machine-learning model in advance. As the user uploads new images, the number of interactions needed to accurately segment the image drops, eventually to zero, enabling rapid annotation of the entire dataset.
In addition, the interactive tool does not require a presegmented image dataset for training, so users don’t need machine-learning expertise or extensive computational resources. She is joined on the paper by Jose Javier Gonzalez Ortiz PhD ’24; John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering; and senior author Adrian Dalca, an assistant professor at Harvard Medical School and MGH, and a research scientist in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The researchers carefully engineered and trained the model on a diverse collection of biomedical imaging data to ensure it had the ability to incrementally improve its predictions based on user input.
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