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Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images
Katy Blumer · Subhashini Venugopalan · Michael Brenner · Jon Kleinberg

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in Interpretable Machine Learning in Healthcare »

We analyze a dataset of retinal images using linear probes: linear regression models trained on some target'' task, using embeddings from a deep convolutional (CNN) model trained on somesource'' task as input. We use this method across all possible pairings of 93 tasks in the UK Biobank dataset of retinal images, leading to ~164k different models. We analyze the performance of these linear probes by source and target task and by layer depth. We observe that representations from the middle layers of the network are more generalizable. 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.

Author Information

Katy Blumer (Google / Cornell)
Subhashini Venugopalan (Google)
Michael Brenner (Google/Harvard)
Jon Kleinberg (Cornell)

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