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Recent machine learning techniques have dramatically changed how we processdigital images. However, the way in which we capture images is still largelydriven by human intuition and experience. This restriction is in part due tothe many available degrees of freedom that alter the image acquisition process(lens focus, exposure, filtering, etc). Here we focus on one such degree offreedom - illumination within a microscope - which can drastically alterinformation captured by the image sensor. We present a reinforcement learningsystem that adaptively explores optimal patterns to illuminate specimens forimmediate classification. The agent uses a recurrent latent space to encode alarge set of variably-illuminated samples and illumination patterns. We trainour agent using a reward that balances classification confidence with imageacquisition cost. By synthesizing knowledge over multiple snapshots, the agentcan classify on the basis of all previous images with higher accuracy than fromnaively illuminated images, thus demonstrating a smarter way to physicallycapture task-specific information.

Towards an Intelligent Microscope: adaptively learned illumination for optimal sample classification