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Akademický článek

Neural network-based processing and reconstruction of compromised biophotonic image data

Autor: Michael John Fanous, Paloma Casteleiro Costa, Çağatay Işıl, Luzhe Huang, Aydogan Ozcan

Publikováno v: Light: Science & Applications, Vol 13, Iss 1, Pp 1-13 (2024)

Abstract In recent years, the integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bi

Externí odkaz: https://doaj.org/article/20ba9b64d6124e50b6933eef4f7af672

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Akademický článek

Virtual histological staining of unlabeled autopsy tissue

Autor: Yuzhu Li, Nir Pillar, Jingxi Li, Tairan Liu, Di Wu, Songyu Sun, Guangdong Ma, Kevin de Haan, Luzhe Huang, Yijie Zhang, Sepehr Hamidi, Anatoly Urisman, Tal Keidar Haran, William Dean Wallace, Jonathan E. Zuckerman, Aydogan Ozcan

Publikováno v: Nature Communications, Vol 15, Iss 1, Pp 1-17 (2024)

Abstract Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand subst

Externí odkaz: https://doaj.org/article/e629233208ca4c589824c397bea9a0fb

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Akademický článek

Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization

Autor: Hanlong Chen, Luzhe Huang, Tairan Liu, Aydogan Ozcan

Publikováno v: Light: Science & Applications, Vol 11, Iss 1, Pp 1-10 (2022)

A deep learning framework, termed Fourier Imager Network (FIN), performs end-to-end phase recovery and image reconstruction from raw holograms, achieving unprecedented success in generalization to new types of samples.

Externí odkaz: https://doaj.org/article/b9587f5cdd86483795a5247ecb40ad9c

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Akademický článek

Cycle-Consistency-Based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems

Autor: Luzhe Huang, Jianing Li, Xiaofu Ding, Yijie Zhang, Hanlong Chen, Aydogan Ozcan

Publikováno v: Intelligent Computing, Vol 2 (2023)

Uncertainty estimation is critical for numerous deep neural network (DNN) applications and has drawn increasing attention from researchers. In this study, we demonstrated an uncertainty quantification approach for DNNs used in inverse problems based

Externí odkaz: https://doaj.org/article/61145aaa4bbb4fad8e517824c01bf48a

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Akademický článek

Few-shot transfer learning for holographic image reconstruction using a recurrent neural network

Autor: Luzhe Huang, Xilin Yang, Tairan Liu, Aydogan Ozcan

Publikováno v: APL Photonics, Vol 7, Iss 7, Pp 070801-070801-8 (2022)

Deep learning-based methods in computational microscopy have been shown to be powerful but, in general, face some challenges due to limited generalization to new types of samples and requirements for large and diverse training data. Here, we demonstr

Externí odkaz: https://doaj.org/article/dc5fc47d65034873804c85efa709cc62

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Akademický článek

Recurrent neural network-based volumetric fluorescence microscopy

Autor: Luzhe Huang, Hanlong Chen, Yilin Luo, Yair Rivenson, Aydogan Ozcan

Publikováno v: Light: Science & Applications, Vol 10, Iss 1, Pp 1-16 (2021)

Abstract Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D image

Externí odkaz: https://doaj.org/article/f585a564349945d89c774c1bff3ac28a

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Akademický článek

Virtual Staining of Defocused Autofluorescence Images of Unlabeled Tissue Using Deep Neural Networks

Autor: Yijie Zhang, Luzhe Huang, Tairan Liu, Keyi Cheng, Kevin de Haan, Yuzhu Li, Bijie Bai, Aydogan Ozcan

Publikováno v: Intelligent Computing, Vol 2022 (2022)

Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining

Externí odkaz: https://doaj.org/article/9711502b292f42e89e4e52c8598b96a1

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