bionlplab/longitudinal_transformer_for_survival_analysis
[npj Digital Medicine] "Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling" by Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H Van Tassel, Kyle Kovacs, Emily Y Chew, Zhiyong Lu, Zhangyang Wang, & Yifan Peng
This project helps ophthalmologists and clinical researchers forecast the future risk of progressive eye diseases like AMD and glaucoma. It takes a patient's history of fundus images, captured over time and at irregular intervals, and outputs dynamic, eye-specific survival curves predicting the time to disease onset. This is for medical professionals and researchers involved in eye care and disease progression studies.
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Use this if you need to dynamically predict the long-term risk of eye diseases using a patient's historical sequence of fundus images.
Not ideal if you only need a static diagnosis of disease presence from a single image at one point in time.
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Python
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MIT
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Last pushed
Dec 18, 2024
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