ArticleBiomedical optics express2019
Learned sensing: jointly optimized microscope hardware for accurate image classification.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Multi-contrast laser endoscopy for in vivo gastrointestinal imaging.Npj imaging · 2026Article
- Diagnostic Accuracy of the National Library of Medicine (NLM) Screener Application in Screening for Malaria Parasites Among Blood Donors at the Korle-Bu Blood Bank, Ghana: A Cross-Sectional Study.Journal of tropical medicine · 2026Article
- Artificial Intelligence in Label-Free Optical Imaging Applications.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Physical twinning for joint encoding-decoding optimization in computational optics: a review.Light, science & applications · 2025Review
- Development and Assessment of Multiple Illumination Color Fourier Ptychographic Microscopy for High Throughput Sample Digitization.Sensors (Basel, Switzerland) · 2024Article
- Review
- Intelligent Beam Optimization for Light-Sheet Fluorescence Microscopy through Deep Learning.Intelligent computing (Washington, D.C.) · 2024Article
- Prediction of malaria positivity using patients' demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment.Tropical diseases, travel medicine and vaccines · 2023Article
- Enhancing Light-Sheet Fluorescence Microscopy Illumination Beams through Deep Design Optimization.bioRxiv : the preprint server for biology · 2023Article
- Phenomic Imaging.Phenomics (Cham, Switzerland) · 2023Review
- Optical ptychography for biomedical imaging: recent progress and future directions [Invited].Biomedical optics express · 2023Review
- Deep learning in optical metrology: a review.Light, science & applications · 2022Review
- Increasing a microscope's effective field of view via overlapped imaging and machine learning.Optics express · 2022Article
- Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review.Frontiers in microbiology · 2022Review
- Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics.Journal of biomedical optics · 2021Article
- Recent advances in point spread function engineering and related computational microscopy approaches: from one viewpoint.Biophysical reviews · 2020Review
- Malaria Screener: a smartphone application for automated malaria screening.BMC infectious diseases · 2020Article
- J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction.IEEE journal of selected topics in signal processing · 2020Article
- Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing.Patterns (New York, N.Y.) · 2020Article
- Learned Integrated Sensing Pipeline: Reconfigurable Metasurface Transceivers as Trainable Physical Layer in an Artificial Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since its invention, the microscope has been optimized for interpretation by a human observer. With the recent development of deep learning algorithms for automated image analysis, there is now a clear need to re-design the microscope's hardware for specific interpretation tasks. To increase the speed and accuracy of automated image classification, this work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network. By adding a "physical layer" to a deep classification network, we are able to jointly optimize for specific illumination patterns that highlight the most important sample features for the particular learning task at hand, which may not be obvious under standard illumination. We demonstrate how our learned sensing approach for illumination design can automatically identify malaria-infected cells with up to 5-10% greater accuracy than standard and alternative microscope lighting designs. We show that this joint hardware-software design procedure generalizes to offer accurate diagnoses for two different blood smear types, and experimentally show how our new procedure can translate across different experimental setups while maintaining high accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.