Development of CNN-based model in predicting patients outcomes
We propose to train a prototypical convolutional neural network on a collection of other datasets available from The Cancer Imaging Archive and apply this to the National Lung Screening Trial to predict patient outcomes using finetuning and linear probing . We will then reverse the process by pretraining a model on the NLST data with the hope of improving predictions in the other clinical trials.
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- Applicability of machine learning in predicting patient outcomes
- Few-shot learning
Michael Gee, Massachusetts General Hospital
William Bradley, Massachusetts General Hospital
Karl Knaub, Massachusetts General Hospital