Summary of Time-to-event Pretraining For 3d Medical Imaging, by Zepeng Huo et al.

Time-to-Event Pretraining for 3D Medical Imaging

by Zepeng Huo, Jason Alan Fries, Alejandro Lozano, Jeya Maria Jose Valanarasu, Ethan Steinberg, Louis Blankemeier, Akshay S. Chaudhari, Curtis Langlotz, Nigam H. Shah

First submitted to arxiv on: 14 Nov 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper introduces a new pretraining framework for 3D medical imaging models that leverages large-scale temporal supervision from paired, longitudinal electronic health records (EHRs). The proposed method, called time-to-event pretraining, addresses the missing context problem in current self-supervised methods by incorporating EHR-derived tasks and time-to-event distributions. This approach improves outcome prediction, achieving significant gains in AUROC and Harrell’s C-index across benchmark tasks without sacrificing diagnostic classification performance.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper is about using computer models to look at medical images like CT scans to predict if someone will get sick or not. Right now, these models can only see what’s happening in the present moment, but they can’t understand how things might change over time. The researchers came up with a new way to train these models that uses information from patient records to help them learn about long-term health outcomes. This helps the models make better predictions and could be used in hospitals to help doctors decide who needs extra care.