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arXiv 2024年

作者： Rahman, Md Sadman Sakib Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

Optical imaging and sensing systems based on diffractive elements have seen massive advances over the last several decades. Earlier generations of diffractive optical processors were, in general, designed to deliver information to an independent system that was separately optimized, primarily driven by human vision or perception. With the recent advances in deep learning and digital neural networks, there have been efforts to establish diffractive processors that are jointly optimized with digital neural networks serving as their back-end. These jointly optimized hybrid (optical+digital) processors establish a new "diffractive language" between input electromagnetic waves that carry analog information and neural networks that process the digitized information at the back-end, providing the best of both worlds. Such hybrid designs can process spatially and temporally coherent, partially coherent, or incoherent input waves, providing universal coverage for any spatially varying set of point spread functions that can be optimized for a given task, executed in collaboration with digital neural networks. In this article, we highlight the utility of this exciting collaboration between engineered and programmed diffraction and digital neural networks for a diverse range of applications. We survey some of the major innovations enabled by the push-pull relationship between analog wave processing and digital neural networks, also covering the significant benefits that could be reaped through the synergy between these two complementary paradigms. Copyright © 2024, The Authors. All rights reserved.

关键词： Diffraction

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Multi-Classification of Retinal Diseases Using a Pyramidal Ensemble Deep Framework

Multi-Classification of Retinal Diseases Using a Pyramidal E...

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IEEE International Conference on Image Processing

作者： Oluwatunmise Akinniyi Imran Razzak Md Mahmudur Rahman Harpal Sandhu Ayman El-Baz Fahmi Khalifa Department of Computer Science School of Computer Mathematical and Natural Sciences Morgan State University Baltimore MD University of New South Wales Sydney NSW Australia Bioengineering Department University of Louisville Louisville KY Electrical and Computer Engineering Department Morgan State University Baltimore MD

Retinal disorders diagnosis is of immense importance for appropriate treatment, i.e., accurate personalized medicine. In this work, a multi-resolutional feature ensemble approach is developed for retinal image classification using optical coherence tomography (OCT) images. Particularly, feature-rich pipeline using pyramidal architecture is designed to extract features from multi-scale inputs using partially-connected networks (PCNet). In addition, higher-order reflectivity features are extracted from the input images and are fused with pyramidal features for classification. The advantage of the hierarchical PCNet structure is that it allowed our system to extract multi-scale information to help in such task, all-at-once classification of the normal and abnormal retina. Namely, the larger input sizes give more global information, while the small inputs focus on local details. Evaluation on public OCT data set of four classes (normal, diabetic macular edema (DME), choroidal neovascularization (CNV), and drusen) and comparison against recent networks demonstrates not only the advantages of the proposed architecture’s ability to produce feature-rich classification, but also highlights tangible advantages, such as network parameter reduction, enhanced feature learning and information flow, while reducing the risk of over fitting.

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MultiWienerNet: Deep learning for fast shift-varying deconvolution

MultiWienerNet: Deep learning for fast shift-varying deconvo...

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Computational Optical Sensing and Imaging, COSI 2021 - Part of OSA Imaging and Applied Optics Congress 2021

作者： Shuai, Richard W. Yanny, Kyrollos Monakhova, Kristina Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States

We present a deep-learning method based on Wiener filters and U-Nets that performs image reconstruction in systems with spatially-varying aberrations. We train on simulated microscopy measurements and test on experime... 详细信息

ISBN: (纸本)9781557528209

关键词： Image reconstruction

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In vivo Detection of NO using Photonic Microring Resonators

In vivo Detection of NO using Photonic Microring Resonators

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CLEO: Science and Innovations, S and I 2022

作者： Hassan, Sakib Zhao, Xuan Schreib, Christian C. Duret, Guillaume Veiseh, Omid Robinson, Jacob T. Electrical & Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Bioengineering Rice University HoustonTX77005 United States

Here we show how photonic microring resonators enable real-time detection of Nitric Oxide (NO) in vivo, which is critical for physiological monitoring particularly during the wound healing process. © Optica Publi... 详细信息

ISBN: (纸本)9781557528209

关键词： Nitric oxide

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Deep Learning-Enabled Classification of HER2 Score in Breast Cancer Using Pyramid Sampling

Deep Learning-Enabled Classification of HER2 Score in Breast...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Selcuk, Sahan Yoruc Yang, Xilin Bai, Bijie Zhang, Yijie Li, Yuzhu Aydin, Musa Unal, Aras Firat Gomatam, Aditya Guo, Zhen Angus, Darrow Morgan Kolodney, Goren Atlan, Karine Haran, Tal Keidar Pillar, Nir Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA United States Bioengineering Department University of California Los AngelesCA United States University of California Los AngelesCA United States Department of Pathology and Laboratory Medicine University of California at Davis SacramentoCA United States Bnai-Zion Medical Center Haifa Israel Hedassah Hebrew University Medical Center Jerusalem Israel David Geffen School of Medicine University of California Los AngelesCA United States

We present an automated, deep learning-based method for HER2 score classification in breast cancer, achieving 85.47% accuracy on tissue microarrays from 300 patients. This method can significantly improve the HER2 eva... 详细信息

关键词： Lung cancer

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Neural networkbased virtual staining of defocused autofluorescence images of labelfree tissue

Neural networkbased virtual staining of defocused autofluore...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zhang, Yijie Huang, Luzhe Liu, Tairan Cheng, Keyi de Haan, Kevin Li, Yuzhu Bai, Bijie Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Mathematics University of California Los AngelesCA90095 United States Department of Surgery David Geffen School of Medicine University of California Los AngelesCA90095 United States

We present a virtual staining framework to rapidly stain defocused autofluorescence images of label-free tissue. This technique saves image acquisition time by lowering the autofocusing precision of a microscope witho... 详细信息

ISBN: (纸本)9781957171258

关键词： Tissue

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Stable high-peak-power fiber supercontinuum generation for adaptive femtosecond biophotonics

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Advanced Photonics Nexus 2024年第4期3卷 126-135页

作者： Geng Wang Jindou Shi Rishyashring R.Iyer Janet E.Sorrells Haohua Tu University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and TechnologyUrbanaIllinoisUnited States University of Illinois at Urbana-Champaign Department of Electrical and Computer EngineeringUrbanaIllinoisUnited States University of Illinois at Urbana-Champaign Department of BioengineeringUrbanaIllinoisUnited States

Broad and safe access to ultrafast laser technology has been hindered by the absence of optical fiber-delivered pulses with tunable central wavelength,pulse repetition rate,and pulse width in the picosecond–femtosecond *** address this long-standing obstacle,we developed a reliable accessory for femtosecond ytterbium fiber chirped pulse amplifiers,termed a fiber-optic nonlinear wavelength converter(FNWC),as an adaptive optical source for the emergent field of femtosecond *** accessory empowers the fixed-wavelength laser to produce fiber-delivered∼20 nJ pulses with central wavelength across 950 to 1150 nm,repetition rate across 1 to 10 MHz,and pulse width across 40 to 400 fs,with a long-term stability of>2000 *** a prototypical label-free application in biology and medicine,we demonstrate the utility of FNWC in real-time intravital imaging synergistically integrated with modern machine learning and largescale fluorescence lifetime imaging microscopy.

关键词： femtosecond biophotonics fiber supercontinuum label-free multiphoton microscopy nonlinear optics and imaging self-supervised denoise

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Development of a ROS2-based Photoacoustic-Robotic Visual Servoing System 23

Development of a ROS2-based Photoacoustic-Robotic Visual Ser...

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Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXIII 2025

作者： Folk, Taylor R. Gubbi, Mardava R. Lediju Bell, Muyinatu A. Department of Bioengineering Harvard University BostonMA United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Department of Computer Science Johns Hopkins University BaltimoreMD United States Department of Oncology Johns Hopkins University School of Medicine BaltimoreMD United States

ISBN: (纸本)9781510683600

A key component of many robotic and surgical procedures is the ability to effectively visualize and track surgical tool tips. In this paper, we introduce a real-time deep learning photoacoustic visual servoing system that uses ROS2 and Moveit2 to make and execute robot path planning decisions in order to track and maintain visualization of tool tips. This system also uses Detectron2 and 2D simulated photoacoustic channel data to train the deep neural network. The performance of this ROS2-based deep learning visual servoing system is compared to that of a deep learning-based visual servoing system that utilizes ROS as its software system, and Detectron and 2D simulated data to train its neural network. Experiments were conducted with a plastisol phantom. Needle tip tracking performance with the ROS2-based visual servoing system outperformed that of the ROS-based system by 23.53% in phantom tissue. These results demonstrate the benefits of upgrading the robot operating system to ROS2 for improved deep learning-based visual servoing and tracking of interventional tool tips. © 2025 SPIE.

关键词： Visual servoing

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Computational Spectroscopy via Large Aperture Meta-Optics

Computational Spectroscopy via Large Aperture Meta-Optics

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Fröch, Johannes E. Colburn, Shane Zhan, Alan Han, Zheyi Fang, Zhuoran Saxena, Abhi Huang, Luocheng Böhringer, Karl F. Majumdar, Arka Department of Physics University of Washington SeattleWA98195 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Tunoptix Fluke Hall 4000 Mason Road 300 SeattleWA98195 United States Department of Bioengineering University of Washington SeattleWA98195 United States Institute for Nano-Engineered Systems University of Washington SeattleWA98195 United States

We demonstrate the concept of a meta-optic based computational spectrometer, combining a meta-optic, which encodes the spectrum of transmitted light into a spatial intensity pattern, that is retrievable through a comp... 详细信息

ISBN: (纸本)9781957171258

关键词： Spectrometers

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LARGE FOV SHORT-WAVE INFRARED META-LENS FOR SCANNING FIBER ENDOSCOPY

arXiv

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arXiv 2022年

作者： Xie, Ningzhi Carson, Matthew D. Fröch, Johannes E. Majumdar, Arka Seibel, Eric Böhringer, Karl F. Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Mechanical Engineering University of Washington SeattleWA98195 United States Department of Electrical and Computer Engineering Department of Physics University of Washington SeattleWA98195 United States Department of Electrical and Computer Engineering Department of Bioengineering University of Washington SeattleWA98195 United States

The scanning fiber endoscope (SFE), an ultra-small optical imaging device with a large field-of-view (FOV) for having a clear forward view into the interior of blood vessels, has great potential in the cardio-vascular disease diagnosis and surgery assistance, which is one of the key applications for short-wave infrared (SWIR) biomedical imaging. The state-of-the-art SFE system uses a miniaturized refractive spherical lens doublet for beam projection. A meta-lens is a promising alternative which can be made much thinner and has fewer off-axis aberrations than its refractive counterpart. We report an SFE system with meta-lens working at 1310nm to achieve a resolution (∼ 140µm at the center of field and the imaging distance of 15mm), FOV (∼ 70◦), and depth-of-focus (DOF ∼ 15mm), which are comparable to a state-of-the-art refractive lens SFE. The use of the meta-lens reduces the length of the optical track from 1.2mm to 0.86mm. The resolution of our meta-lens based SFE drops by less than a factor of 2 at the edge of the FOV, while the refractive lens counterpart has a ∼ 3 times resolution degradation. These results show the promise of integrating a meta-lens into an endoscope for device minimization and optical performance improvement. © 2022, CC BY-NC-ND.

关键词： Infrared radiation

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