See you in two years!
11-16 April 2026
Conference 13006 > Paper 13006-1
Paper 13006-1

On-the-fly Raman microscopy guaranteeing the accuracy of discrimination (Invited Paper)

8 April 2024 • 11:20 - 11:50 CEST | Etoile B, Niveau/Level 1

Abstract

Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back “optimal” illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Here accurate discrimination means that a user can determine an allowance error rate δ a priori to ensure that the diagnosis can be accurately accomplished with probability greater than (1 − δ) × 100%. We present our algorithm and our simulation studies using Raman images in the diagnosis of follicular thyroid carcinoma, and show that this protocol can accelerate in speedy and accurate diagnoses faster than the point scanning Raman microscopy that requires the full detailed scanning over all pixels. Given a descriptor based on Raman signals to quantify the degree of the predefined quantity to be evaluated, e.g., the degree of cancers, anomaly or defects of materials, the on-the-fly Raman image microscopy evaluates the upper and lower confidence bounds in addition to the sample average of that quantity based on finite point illuminations, and then the bandit algorithm feedbacks the desired illumination pattern to accelerate the detection of the anomaly, during the measurement to the microscope. Several updated realizations of the programmable illumination microscope using a spatial light modulator and line illumination will be presented.

Presenter

Hokkaido Univ. (Japan)
Tamiki Komatsuzaki holds a professor of Research Institute for Electronic Science, Hokkaido University, Japan, since 2007, moving from Kobe University, Japan (1999-2007). Previously, he was appointed as research associate at Department of Chemistry, University of Chicago, USA (1996-1999). He is an author of more than 130 publications in peer-reviewed scientific journals over interdisciplinary researches, covering from extraction of state space networks and energy landscapes from noisy single molecule time series in measurements, to bridging information science and Raman microscope to accelerate cell or tissue diagnosis while preserving its accuracy, supported by Japan Science and Technology/CREST, Japan entitled “Development of Accelerated Measurement Technology for Cell Diagnosis by bridging Single-Cell Raman Imaging and Information Science”.
Application tracks: AI/ML
Presenter/Author
Hokkaido Univ. (Japan)