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Towards an intelligent microscope: Adaptively learned illumination for optimal sample classification

Publication ,  Journal Article
Chaware, A; Cooke, CL; Kim, K; Horstmeyer, R
Published in: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
May 1, 2020

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

Duke Scholars

Published In

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

DOI

ISSN

1520-6149

Publication Date

May 1, 2020

Volume

2020-May

Start / End Page

9284 / 9288
 

Citation

APA
Chicago
ICMJE
MLA
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Chaware, A., Cooke, C. L., Kim, K., & Horstmeyer, R. (2020). Towards an intelligent microscope: Adaptively learned illumination for optimal sample classification. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2020-May, 9284–9288. https://doi.org/10.1109/ICASSP40776.2020.9054477
Chaware, A., C. L. Cooke, K. Kim, and R. Horstmeyer. “Towards an intelligent microscope: Adaptively learned illumination for optimal sample classification.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2020-May (May 1, 2020): 9284–88. https://doi.org/10.1109/ICASSP40776.2020.9054477.
Chaware A, Cooke CL, Kim K, Horstmeyer R. Towards an intelligent microscope: Adaptively learned illumination for optimal sample classification. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2020 May 1;2020-May:9284–8.
Chaware, A., et al. “Towards an intelligent microscope: Adaptively learned illumination for optimal sample classification.” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, May 2020, pp. 9284–88. Scopus, doi:10.1109/ICASSP40776.2020.9054477.
Chaware A, Cooke CL, Kim K, Horstmeyer R. Towards an intelligent microscope: Adaptively learned illumination for optimal sample classification. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2020 May 1;2020-May:9284–9288.

Published In

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

DOI

ISSN

1520-6149

Publication Date

May 1, 2020

Volume

2020-May

Start / End Page

9284 / 9288