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Found 5 papers, 1 papers with code
Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images
We find that some target tasks are easily predicted irrespective of the source task, and that some other target tasks are more accurately predicted from correlated source tasks than from embeddings trained on the same task.
The Algorithmic Automation Problem: Prediction, Triage, and Human Effort
In a wide array of areas, algorithms are matching and surpassing the performance of human experts, leading to consideration of the roles of human judgment and algorithmic prediction in these domains.
Direct Uncertainty Prediction for Medical Second Opinions
Our central methodological finding is that Direct Uncertainty Prediction (DUP), training a model to predict an uncertainty score directly from the raw patient features, works better than Uncertainty Via Classification, the two-step process of training a classifier and postprocessing the output distribution to give an uncertainty score.
Deep learning for predicting refractive error from retinal fundus images
Mean absolute error (MAE) of the algorithm's prediction compared to the refractive error obtained in the AREDS and UK Biobank.
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning
Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses.
