Fast phase retrieval in off-axis digital holographic microscopy through deep learning

Zhang, Gong; Guan, Tian; Shen, Zhiyuan; Wang, Xiangnan; Hu, Tao; Wang, Delai; He, Yonghong; Xie, Na

doi:10.1364/oe.26.019388

Cited by 93 publications

(28 citation statements)

References 32 publications

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“…In recent years, deep learning receives much attention in many research fields including optical design [1,2] and optical imaging [3]. In previous works, deep learning has been extensively applied for many optical imaging problems including phase retrieval [4][5][6][7], microscopic image enhancement [8][9], scattering imaging [10][11], holography [12][13][14][15][16][17][18], single-pixel imaging [19,20], super-resolution [21][22][23][24], Fourier ptychography [25][26][27], optical interferometry [28,29], wavefront sensing [30,31], and optical fiber communications [32].…”

Section: Introductionmentioning

confidence: 99%

Does deep learning always outperform simple linear regression in optical imaging?

et al. 2020

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Deep learning has been extensively applied in many optical imaging problems in recent years. Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom investigated. In this work, we show that conventional linear-regression-based methods can outperform the previously proposed deep learning approaches for two black-box optical imaging problems in some extent. Deep learning demonstrates its weakness especially when the number of training samples is small. The advantages and disadvantages of linear-regression-based methods and deep learning are analyzed and compared. Since many optical systems are essentially linear, a deep learning network containing many nonlinearity functions sometimes may not be the most suitable option.

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Section: Introductionmentioning

confidence: 99%

Does deep learning always outperform simple linear regression in optical imaging?

et al. 2020

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“…In recent years, DL approaches have been applied to LDIH, including reconstruction improvement [12,13], phase retrieval [14], and classification and monitoring of various biological samples [3,15,16]. Min et al developed an artificial intelligence diffraction analysis (AIDA) platform to make automated, rapid, high-throughput, and accurate cancer cell analysis [3].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Deep Learning for Breast Cancer Cell Phenotyping Using Diffraction Images from Lens-Free Digital In-Line Holography

Cao

Min

et al. 2021

Preprint

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Lens-free digital in-line holography (LDIH) produces cellular diffraction patterns (holograms) with a large field of view that lens-based microscopes cannot offer. It is a promising diagnostic tool allowing comprehensive cellular analysis with high-throughput capability. Holograms are, however, far more complicated to discern by the human eye, and conventional computational algorithms to reconstruct images from hologram limit the throughput of hologram analysis. To efficiently and directly analyze holographic images from LDIH, we developed a novel deep learning architecture called a holographical deep learning network (HoloNet) for cellular phenotyping. The HoloNet uses holo-branches that extract large features from diffraction patterns and integrates them with small features from convolutional layers. Compared with other state-of-the-art deep learning methods, HoloNet achieved better performance for the classification and regression of the raw holograms of the breast cancer cells stained with well-known breast cancer markers, ER/PR and HER2. Moreover, we developed the HoloNet dual embedding model to extract high-level diffraction features related to breast cancer cell types and their marker intensities of ER/PR and HER2 to identify previously unknown subclusters of breast cancer cells. This hologram embedding allowed us to identify rare and subtle subclusters of the phenotypes overlapped by multiple breast cancer cell types. We demonstrate that our HoloNet efficiently enables LDIH to perform a more detailed analysis of heterogeneity of cell phenotypes for precise breast cancer diagnosis.

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“…In this process, the twin image, the zero-order image and the aberration components are simultaneously eliminated. Most of these algorithms are based on UNet model [33], which is a classical paired training method, and has been widely used in holographic field [34], [35]. However, the hologram and the corresponding object real distribution need to be strictly paired in the training process.…”

Section: Introductionmentioning

confidence: 99%

Digital Holographic Reconstruction Based on Deep Learning Framework With Unpaired Data

Yin¹,

Gu²,

Zhang³

et al. 2020

IEEE Photonics J.

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Convolutional neural network (CNN) has great potentials in holographic reconstruction. Although excellent results can be achieved by using this technique, the number of training and label data must be the same and strict paired relationship is required. Here, we present a new end-to-end learning-based framework to reconstruct noise-free images in absence of any paired training data and prior knowledge of object real distribution. The algorithm uses the cycle consistency loss and generative adversarial network to implement unpaired training method. It is demonstrated by the experiments that high accuracy reconstruction images can be obtained by using unpaired training and label data. Moreover, the unpaired feature of the algorithm makes the system robust to displacement aberration and defocusing effect.

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Fast phase retrieval in off-axis digital holographic microscopy through deep learning

Cited by 93 publications

References 32 publications

Does deep learning always outperform simple linear regression in optical imaging?

Does deep learning always outperform simple linear regression in optical imaging?

Interpretable Deep Learning for Breast Cancer Cell Phenotyping Using Diffraction Images from Lens-Free Digital In-Line Holography

Digital Holographic Reconstruction Based on Deep Learning Framework With Unpaired Data

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