We developed a fast Raman spectroscopic discrimination system based on a slit-scanning confocal microscope and machine learning. The speed of discrimination was improved by reducing the number of measurements, without measuring all points in the field of view. During discrimination, the system continues to evaluate the spectra already obtained, which guarantees the accuracy of the discrimination and enables early detection of anomalies by optimizing the measurement positions. We performed discrimination using a mixture of polystyrene (PS) and polymethyl methacrylate (PMMA) microbeads as a sample to mimic cancer tissue and that of fatty liver tissue using mouse liver tissue samples. The results showed that the discrimination was about 2-11 times faster than that by slit scanning confocal microscopy.
KEYWORDS: Raman spectroscopy, Light sources and illumination, Machine learning, Medical research, Random forests, Microscopy, Microscopes, Engineering, Diagnostics, Decision trees
We propose a method that combines high-speed Raman imaging with a machine learning technique, multi-armed bandit, to achieve rapid and accurate identification of samples under observation. First, our method dvides the field of view of the sample into small sections, and it returns either ’positive’ or ’negative’ based on whether the sections with high anomaly indices exceed a certain proportion. Moreover, the points to be measured are determined dynamically and automatically generating a series of optimal illumination patterns.
We developed spontaneous Raman microscopy using Bandit algorithm to realize fast diagnosis of the existence of anomalies or not with guaranteeing accuracy. The algorithm evaluates obtained Raman spectra during measurement to judge if the diagnosis is completed with ensuring an allowance error rate that users decided and also to generate optimal illumination patterns for the next irradiation which are optimized to accelerate the detection of anomaly. We present our simulation and experimental studies to show that our system can accelerate more than a few tens times faster than line-scanning Raman microscopy which requires full scanning over all pixels.
We present our recent study combined multi-armed Bandits algorithm in reinforcement learning with spontaneous Raman microscope for the acceleration of the measurements by designing and generating optimal illumination pattern “on the fly” during the measurements while keeping the accuracy of diagnosis. We present our simulation and experimental studies using Raman images in the diagnosis of follicular thyroid carcinoma and non-alcoholic fatty liver disease, and show that this protocol can accelerate more than a few tens times in speedy and accurate diagnoses faster than line-scanning Raman microscope that requires the full detailed scanning over all pixels.
The on-the-fly Raman image microscopy designs to accelerate measurements by combining one of reinforcement machine learning techniques, bandit algorithm utilized in the Monte Carlo tree search in alpha-GO, and a programmable illumination system. Given a descriptor based on Raman signals to quantify the likelihood of the predefined quantity to be evaluated, e.g., the degree of cancers, 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/line illuminations, and then the bandit algorithm feedbacks the desired illumination pattern to accelerate the detection of the anomaly, during the measurement to the microscope.
Most conventional bandit algorithms assume that infinite number of measurements or samples provides us with 100% accuracy. However, in Raman measurements we should develop both a Raman descriptor to quantify the degree of anomaly, and a new algorithm to take into account the finite accuracy lower than 100%. This microscope can also be applied to other problems, besides detection of cancer cells, such as anomaly or defects of materials. The algorithm itself is also beneficial and transferrable to the other microscopes such as infrared image microscope.
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