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- W4313304976 abstract "Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification." @default.
- W4313304976 created "2023-01-06" @default.
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- W4313304976 date "2022-12-27" @default.
- W4313304976 modified "2023-09-27" @default.
- W4313304976 title "From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis" @default.
- W4313304976 doi "https://doi.org/10.48550/arxiv.2212.13535" @default.
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