ORCID Profile
0000-0002-9104-2315
Current Organisation
Nanyang Technological University
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Publisher: Springer Science and Business Media LLC
Date: 24-02-2022
DOI: 10.1038/S41467-022-28702-0
Abstract: Large-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical computing. Traditional experimental implementations need N 2 units such as Mach-Zehnder interferometers (MZIs) for an input dimension N to realize typical computing operations (convolutions and matrix multiplication), resulting in limited scalability and consuming excessive power. Here, we propose the integrated diffractive optical network for implementing parallel Fourier transforms, convolution operations and application-specific optical computing using two ultracompact diffractive cells (Fourier transform operation) and only N MZIs. The footprint and energy consumption scales linearly with the input data dimension, instead of the quadratic scaling in the traditional ONN framework. A ~10-fold reduction in both footprint and energy consumption, as well as equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the MNIST and Fashion-MNIST datasets. The integrated diffractive optical network (IDNN) chip demonstrates a promising avenue towards scalable and low-power-consumption optical computational chips for optical-artificial-intelligence.
Publisher: Elsevier BV
Date: 08-2020
Publisher: MDPI AG
Date: 07-12-2020
DOI: 10.3390/MI11121084
Abstract: High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles. Imaging acquisition using camera is capable of observing in idual nanoparticles in real time, but the accuracy is compromised by the image defocusing and instrumental calibration. In this work, a machine learning-based pipeline is developed to facilitate a high accuracy imaging-based particle sizing. The pipeline consists of an image segmentation module for cell identification and a machine learning model for accurate pixel-to-size conversion. The results manifest a significantly improved accuracy, showing great potential for a wide range of applications in environmental sensing, biomedical diagnostical, and material characterization.
Publisher: Elsevier BV
Date: 05-2022
DOI: 10.1016/J.SCHRES.2021.06.011
Abstract: The ability of automatic feature learning makes Convolutional Neural Network (CNN) potentially suitable to uncover the complex and widespread brain changes in schizophrenia. Despite that, limited studies have been done on schizophrenia identification using interpretable deep learning approaches on multimodal neuroimaging data. Here, we developed a deep feature approach based on pre-trained 2D CNN and naive 3D CNN models trained from scratch for schizophrenia classification by integrating 3D structural and diffusion magnetic resonance imaging (MRI) data. We found that the naive 3D CNN models outperformed the pretrained 2D CNN models and the handcrafted feature-based machine learning approach using support vector machine during both cross-validation and testing on an independent dataset. Multimodal neuroimaging-based models accomplished performance superior to models based on a single modality. Furthermore, we identified brain grey matter and white matter regions critical for illness classification at the in idual- and group-level which supported the salience network and striatal dysfunction hypotheses in schizophrenia. Our findings underscore the potential of CNN not only to automatically uncover and integrate multimodal 3D brain imaging features for schizophrenia identification, but also to provide relevant neurobiological interpretations which are crucial for developing objective and interpretable imaging-based probes for prognosis and diagnosis in psychiatric disorders.
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: IEEE
Date: 03-2017
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D1RA02869C
Abstract: Conventional deep neural networks use simple classifiers to obtain highly accurate results. However, they have limitations in practical applications. This study demonstrates a robust deep metric neural network model for rare bioparticle detection.
Publisher: Wiley
Date: 20-02-2021
DOI: 10.1002/CYTO.A.24321
Abstract: Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning‐enabled high‐throughput system for predicting Cryptosporidium and Giardia in drinking water. This system combines imaging flow cytometry and an efficient artificial neural network called MCellNet, which achieves a classification accuracy .6%. The system can detect Cryptosporidium and Giardia with a sensitivity of 97.37% and a specificity of 99.95%. The high‐speed analysis reaches 346 frames per second, outperforming the state‐of‐the‐art deep learning algorithm MobileNetV2 in speed (251 frames per second) with a comparable classification accuracy. The reported system empowers rapid, accurate, and high throughput bioparticle detection in clinical diagnostics, environmental monitoring and other potential biosensing applications.
Publisher: Springer Science and Business Media LLC
Date: 19-01-2021
DOI: 10.1038/S41467-020-20719-7
Abstract: Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.
No related grants have been discovered for Xudong Jiang.