Mapping information and light: Trends of AI-enabled metaphotonics

Lee, Seokho; Park, Cherry; Rho, Junsuk

doi:10.1016/j.cossms.2024.101144

Cited by 6 publications

References 196 publications

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Multimodality Image Segmentation Embedded with Metasurface Encoder

Xu,

Wu,

Jin

et al. 2024

Annalen der Physik

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Feature dimensionality plays an important role for modern medical diagnosis and image processing. In this work, the study introduces an optoelectronic neural network for multimodal image segmentation, which dramatically improves computing speed and decreases imaging acquisition cost in brain tumor diagnostics. Multi‐layer metasurfaces are utilized as an image preprocessor that reduces image dimensionality at the physical layer. The low‐dimensional image is then processed to a U‐Net semantic segmentation network, to handle the complex and heterogeneous nature of brain image data. By using diffractive neural network, the metasurface encoder is optimized and physically constructed with high‐efficiency transmission metasurfaces. The entire optoelectronic network attains a structural similarity index measure (SSIM) of 96%, demonstrating its potential to revolutionize on‐site medical image processing with its high precision in segmenting brain imaging data.

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Multimodality Image Segmentation Embedded with Metasurface Encoder

Xu,

Wu,

Jin

et al. 2024

Annalen der Physik

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Deep Learning‐Driven Robust Glucose Sensing and Fruit Brix Estimation Using a Single Microwave Split Ring Resonator

Lee,

Kim,

Yang

et al. 2024

Laser & Photonics Reviews

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Extracting the desired information from sensor data with various internal and external effects is a significant challenge in sensor applications. Difficult‐to‐control factors such as temperature, humidity, and sample position can significantly affect the stability and reliability of sensor data. In this paper, a deep learning‐based glucose sensing method that is robust to variations in sample position is proposed. It is shown that the variations in sample position affect the sensor data measured by the designed split ring resonator‐based microwave sensor. Then, artificial neural network and 1D convolutional neural network (CNN) models are evaluated for extracting glucose concentration information from the sensor data measured at random sample positions. The concentration of the glucose solution ranged from 1% to 23% (2% increments). The 1D CNN with all frequencies (0.5–18 GHz) of the and datasets outperformed the other model, with a mean absolute error (MAE) of 0.695% and a mean squared error (MSE) of 0.876 evaluated via cross‐validation. The study demonstrated that the sensor system can be applied in real life by performing fruit Brix estimation based on transfer learning of the previous 1D CNN network, and the MAE and MSE are 0.450% and 0.305, respectively.

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