How to combine machine learning for microscopic image quality control?
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Machine learning is used in various ways for microscopic image quality control. One approach is to automatically estimate the quality level of special steels based on microscopic images . This helps eliminate the effect of personal differences between inspectors and reduces inspection costs. Another method involves using machine learning to correct optical aberrations in high-speed and high-resolution scanning microscopic imaging systems . By training a correction model with input from distortion focus spots, the system achieves fast aberration correction and improves the quality of focus spots. Additionally, machine learning is utilized in an automated quality control protocol for visual cellular phenotypes . This protocol identifies and removes common image-based aberrations, such as focus blur and image saturation, to ensure accurate downstream analysis. Furthermore, a convolutional neural network is employed to analyze the quality of pathological microscopic images, improving diagnosis intelligence and accuracy . Lastly, an optimized self-adaptive microscopic imaging method uses machine learning to optimize imaging results, eliminate image distortion, and improve imaging speed . These approaches demonstrate the effectiveness of combining machine learning with microscopic image quality control.
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| Papers (4) | Insight |
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| The paper describes a method that uses a convolutional neural network for training image quality analysis models to identify various quality problems in pathological microscopic images. This improves the quality of images used for diagnosis. | |
47 Citations | The paper describes a protocol for automated quality control in high-throughput imaging experiments using machine learning. It explains how machine learning methods can be applied to discern which measurements and cutoffs best detect different types of quality control problems. |
Patent 29 Mar 2019 2 Citations | The paper describes a method where machine learning is used to train a correction model based on the light intensity distribution and Zernike polynomial coefficients of distortion focus spots, which is then used to correct optical aberrations in microscopic imaging. |
25 Jul 2021 2 Citations | The paper proposes an automatic-quality-level-estimation system for inspecting steel microstructures based on machine learning to eliminate personal differences between inspectors and reduce inspection costs. |
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