Science, Space and Technology News 2024
Researchers from MIT and elsewhere developed a machine-learning framework that can generate multiple plausible answers when asked to identify potential disease in medical images. By capturing the inherent ambiguity in these images, this technique could prevent clinicians from missing crucial information that could inform diagnoses. Credit: MIT News; iStock
Importantly, Tyche can tackle new segmentation tasks without needing to be retrained. Training is a data-intensive process that involves showing a model many examples and requires extensive machine-learning experience. Their co-authors include Hallee Wong, a graduate student in electrical engineering and computer science; Jose Javier Gonzalez Ortiz PhD ’23; Beth Cimini, associate director for bioimage analysis at the Broad Institute; and John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering. Rakic will present Tyche at the IEEE Conference on Computer Vision and Pattern Recognition, where Tyche has been selected as a highlight..
The researchers found that just 16 example images, called a “context set,” is enough for the model to make good predictions, but there is no limit to the number of examples one can use. The context set enables Tyche to solve new tasks without retraining. They also modified the training process so it is rewarded by maximizing the quality of its best prediction.
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