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检索条件"任意字段=Conference on Data Science for Photonics and Biophotonics"

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Advanced Holographical and Physics Inspired Deep Learning Approaches for Image Transmission through Multimode Optical Fiber

Advanced Holographical and Physics Inspired Deep Learning Ap...

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conference on data science for photonics and biophotonics

作者： Kazemzadeh, Mohammadrahim Collard, Liam Piscopoa, Linda Pisanoa, Filippo Ciraci, Cristian De Vittorio, Massimo Pisanello, Ferruccio Ist Italiano Tecnol Ctr Biomol Nanotechnol I-73010 Arnesano LE Italy RAISE Ecosyst Genoa Italy Univ Salento Dipartimento Ingn Innovaz I-73100 Lecce Italy Univ Padua Dept Phys & Astron G Galilei Via Marzolo 8 I-35131 Padua Italy

ISBN: (纸本)9781510673410;9781510673403

Recent strides in data-driven and deep learning methods have empowered image and wavefront reconstruction in such environments. This breakthrough finds promising roles in biomedical applications like image transmission and holography. Yet, the reconstructed image quality relies on deep learning model effectiveness in understanding transmission mechanisms. In our presentation, we propose two enhancements. First, employs a novel deep learning architecture inspired by light physics, showcasing enhanced image reconstruction quality and broad problem generalization. The second one is an optical method which boosts data variance through holographic encoding, enabling multi-channel image transmission and improved data fusion via deep learning.

关键词： Deep learning Holography Image transmission Physics RGB color model data modeling Image restoration Optical fibers Speckle Turbidity

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Using Shapley additive explanations and saliency for interpretation of multiclass classification of PDX spectral data with deep neural networks

Using Shapley additive explanations and saliency for interpr...

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conference on data science for photonics and biophotonics

作者： Rafsanjani, Mohd Rifqi Dooney, Alison Suresh, Rahul O'Farrell, Alice C. Jarzabek, Monika A. Shiels, Liam P. Byrne, Annette T. Prehn, Jochen H. Meade, Aidan D. Technol Univ Dublin Sch Phys Clin & Optometr Sci City Campus Grangegorman Ireland Technol Univ Dublin Ctr Radiat & Environm Sci FOCAS Res Inst Grangegorman Ireland Royal Coll Surgeons Ireland Dept Physiol & Med Phys Dublin Ireland

ISBN: (纸本)9781510673410;9781510673403

The identification of spectral markers differentiating disease states when using spectral data is challenging in the context of modelling with deep neural networks, particularly in scenarios where classification models are developed with multiple classes. While a number of approaches do exist which can provide an insight into the features which are learnt by deep learning models, in biophotonics and chemical imaging these have received relatively little attention. In the present work we pilot the use of Fourier Transform chemical imaging with two deep-learning interpretation approaches within the context of a multi-class classification problem. Fully connected neural networks are developed on unfolded chemical imaging data captured on patient-derived xenografts developed from a colorectal cancer model. Separately, Shapley additive explanations and saliency approaches are used to derive feature sets which are discriminatory for class within this experimental model of colorectal cancer. Preliminary results suggest that Shapley additive explanations provide differentiating spectral sets which may not be derived with saliency, although the feature sets which are identified are dependent upon spectral pretreatment methodology. A dual approach which employs both strategies may be an effective strategy for the identification of feature sets in this context.

关键词： Colorectal cancer Fourier Transform Infrared (FTIR) chemical imaging deep learning Shapley additive explanations (SHAP) Saliency

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Explainable Machine Learning for the Multiclass Classification of Diffuse Reflectance Spectroscopy Signals in Orthopaedic Applications

Explainable Machine Learning for the Multiclass Classificati...

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conference on data science for photonics and biophotonics

作者： Rossberg, Nicola Li, Celina L. Andersson-Engels, Stephan O'Sullivan, Barry Komolibus, Katarzyna Visentina, Andrea Univ Coll Cork Dept Comp Sci & Informat Technol Cork Ireland Univ Coll Cork Ctr Res Training Artificial Intelligence Cork Ireland Tyndall Natl Inst IPIC Biophoton Tyndall Cork Ireland Univ Coll Cork Sch Phys Cork Ireland Univ Coll Cork Insight Ctr Data Analyt Cork Ireland

ISBN: (纸本)9781510673410;9781510673403

Revision total hip arthroplasty suffers from low visibility with intra-body navigation hinging primarily on auditory and tactile cues. Consequently, the risk of surgical injury increases. One proposition to increase surgical precision is integrating an algorithm which classifies encountered tissues based on their reflectance spectra into the surgical tools. Previous works have developed machine learning applications for the automatic, binary, classification of tissue based on diffuse reflectance spectroscopy (DRS) signals and exploratory investigations have successfully integrated DRS probes into surgical devices including surgical drills. However, one problem with these studies is a lack of transparency in the algorithms, which is important to increase practitioners' trust and prevent bias. This study developed four machine learning algorithms which simultaneously classified broadband DRS signals (355 - 1850 nm) of six ovine tissue classes. The algorithms were Linear Discriminant Analysis (LDA), Random Forrest, Convolutional Neural Network (CNN), and a Transformer model. Class-wise wavelength importance was visualized using model-based methods to understand classification mechanisms and increase model-explainability. It is concluded that CNNs hold the potential for successful initial device design and medical integration.

关键词： biophotonics Deep Learning Transparent Feature Selection Machine Learnin Diffuse Reflectance Spectroscopy Explainabilty and XAI

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Spike detection algorithms for Raman spectroscopy: A comparative study

Spike detection algorithms for Raman spectroscopy: A compara...

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conference on data science for photonics and biophotonics

作者： Georgiev, Georgi Portela, Raquel Banares, Miguel A. Coca-Lopez, Nicolas Bulgarian Acad Sci Inst Nucl Res & Nucl Energy Sofia Bulgaria CSIC Inst Catalisis & Petroleoquim ICP Calle Marie Curie 2 Madrid 28049 Spain

ISBN: (纸本)9781510673410;9781510673403

Energetic charged particles from the secondary cosmic radiation may interact with Raman measuring devices, leading to local charge anomalies in the signal from the CCD detector represented as single to few samples wide spikes in the resulting Raman spectrum. If not handled properly, these spikes may lead to errors in subsequent data processing steps. There are no practical means to shield the energetic muons, thus spike removal is a common and fundamental step in Raman spectral data pre-treatment. Commercial software from Raman instrument manufacturers usually provide spike removal tools, but the algorithms are not available to users and automation or control of the corrections is impossible. Open-source tools for spike removal are also limited. Moreover, the performance of these tools is rarely validated or benchmarked. The present work is an introduction to a bigger study which aims to perform a systematic comparison of spike removal algorithms. The methodology consists of artificially introduced spikes on a spike-free spectrum. Minimum detectable spike amplitude for four different algorithms is studied by performing a scan, varying the location and amplitude of the artificial spikes. Studied algorithms are made available in RamanChada2 library.

关键词： Spike removal cosmic rays Raman spectroscopy data processing algorithms

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Active learning concept for materials process optimization of non-fullerene organic photovoltaic using small datasets

Active learning concept for materials process optimization o...

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conference on data science for photonics and biophotonics

作者： Almalki, Majed Lachiche, Nicolas Chapuis, Yves Andre Univ Strasbourg ICube Lab Engn Comp Sci & Imagery F-67412 Illkirch Graffenstaden France

ISBN: (纸本)9781510673410;9781510673403

The rise of materials informatics and artificial intelligence (AI)-based computational discovery of materials provides new sources of research directions for materials science. New research demonstrates the enormous potential for experimentalists in photonics for photovoltaic materials to increase the rate of screening and optimization of materials properties and related devices. AI for materials is not only interested in the accuracy of predictive models but also in the effect of data size. Recent investigations have shown significant progress in AI for small data by combining a design of experiments (DoE) approach with machine learning (ML) analysis, which enables experimentalists to use scarce resources more effectively for materials optimization with a higher probability of arriving at a true optimum. In this work, we propose an alternative approach to DoE associated with ML by using the concept of active learning (AL). AL is well appropriate in industry and physical sciences, where there is a strong need to minimize the number of costly experiments necessary to train predictive models. We focus on optimizing processes of organic photovoltaic (OPV) cells. The manufacturing of OPV devices requires on the case of having a very small labeling budget, about a few dozen data points, and developing a simple and fast method for practical AL with a model selection. Then, we discusse the challenges in anticipating the data-driven process design, such as the complexity of the experimental approach of OPV cells, the diversity of experiment parameters, and the necessary programming ability.

关键词： Material sciences organic photovoltaic (OPV) small data design of experiments (DoE) machine learning (ML) active learning (AL) optimization.

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Fusion and integration pipelines for optical and chemical imaging data for clinical interpretation in ex-vivo diagnosis

Fusion and integration pipelines for optical and chemical im...

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conference on data science for photonics and biophotonics

作者： Rafsanjani, Mohd Rifqi Jirstrom, Karin Rahman, Arman Prehn, Jochen H. M. Gallagher, William Meade, Aidan D. Technol Univ Dublin Ctr Radiat & Environm Sci FOCAS Res Inst Dublin Ireland Technol Univ Dublin Sch Phys Clin & Optometr Sci Cent Quad City Campus Grangegorman Ireland Lund Univ Dept Clin Sci Div Oncol & Therapeut Pathol Lund Sweden Skane Univ Hosp Lund Sweden Univ Coll Dublin UCD Sch Biomol & Biomed Sci UCD Conway Inst Dublin Ireland Royal Coll Surgeons Ireland Dept Physiol & Med Phys 123 St Stephens Green Dublin 2 Ireland

ISBN: (纸本)9781510673410;9781510673403

In the realm of ex-vivo diagnosis, the integration of optical and chemical imaging data has emerged as a transformative approach, offering a comprehensive understanding of biological specimens at a molecular level. Chemical imaging of human tissue specimens provides an all-digital label-free approach to imaging in objective histopathology, though it requires reference to gold standard pathological (e.g. haematoxylin and eosin (H+E) stained) images for pathological interpretation. Optical imaging techniques, such as microscopy and spectroscopy, provide detailed spatial information, capturing morphological features with high resolution. Concurrently, chemical imaging methods, including mass spectrometry and Raman spectroscopy, offer insights into molecular composition. The challenge lies in harnessing the complementary strengths of these disparate modalities to extract a holistic understanding of the sample. In this work we present the results of several image alignment approaches for fusion and integration of chemical and pathological imaging data, demonstrating that the process of corner detection is crucial towards precise image alignment.

关键词： Image fusion Image registration Image segmentation Corner detection Imaging spectroscopy Image processing Tissues data fusion FT-IR spectroscopy

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A comprehensive pipeline to integrate preprocessing and machine learning techniques for accurate classification in Raman spectroscopy

A comprehensive pipeline to integrate preprocessing and mach...

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conference on data science for photonics and biophotonics

作者： Innocente, Simone Maryam, Siddra Andersson-Engels, Stefan Komolibus, Katarzyna Gautam, Rekha Visentin, Andrea Biophoton Tyndall Natl Inst Cork Ireland UCC Sch Comp Sci & Informat Technol Cork Ireland UCC Insight Ctr Data Analyt Cork Ireland

ISBN: (纸本)9781510673410;9781510673403

Raman spectroscopy, a non-invasive analytical method, offers insights into molecular structures and interactions in various liquid and solid samples with applications ranging from material science, and chemical analysis to medical diagnostics. Preprocessing of Raman spectra is vital to remove interferences like background signals and calibration errors, ensuring precise data extraction. Artificial intelligence, particularly machine learning (ML), aids in extracting valuable information from complex datasets. However, effective data preprocessing proves to be crucial as it can influence model robustness. This study addresses the integration of preprocessing and ML algorithms, often treated as distinct identities despite their intrinsic interconnection, in Raman spectra of blood samples from patients suffering from ovarian cancer. Optimal preprocessing configuration may not always be evident due to the complexity of spectral data. There are numerous options available for background corrections, normalization, outlier removal, noise filtering, and dimension reduction algorithms for Raman spectra. Moreover, hyperparameter tuning is required to detect the best choices for the preprocessing steps. In this work, we present a pipeline to co-optimize preprocessing techniques and ML classification methods to promote objective selection and minimize processing time. In our approach, preprocessing methods are not chosen arbitrarily but rather systematically evaluated to enhance the robustness of the models. These criteria focus on ensuring that the model performs well not only on the training data but also on unseen data, thus reducing the risk of overfitting and improving the generalization capability of the model. This systematic approach would reduce the time for new studies by detecting the most suitable preprocessing steps and hyperparameters needed and building a robust model for the task.

关键词： Raman spectroscopy Preprocessing Machine Learning Pipeline Classification Ovarian Cancer

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Three-Layer Matrix Ring data Center Interconnect Based on FSO Technology

Three-Layer Matrix Ring Data Center Interconnect Based on FS...

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2024 Asia Communications and photonics conference, ACP 2024 and International conference on Information photonics and Optical Communications, IPOC 2024

作者： Su, Jianning Kong, Qian Lu, Xiaowei Liu, Liming Yi, Zichuan Pan, Xinjian School of Electronic and Information Engineering University of Electronic Science and Technology of China Zhongshan Institute Zhongshan528402 China College of Physics and Optoelectronic Engineering Shenzhen University SZU Shenzhen 518060 China Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen Key Lab of Micro-Nano Photonic Information Technology State Key Laboratory of Radio Frequency Heterogeneous Integration College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China

This paper proposes a three-layer grid matrix ring data center interconnect using Free Space Optical Communication as the inter-layer connection technology, which simplifies the wiring complexity and offers more diver... 详细信息

ISBN: (纸本)9798350379266

关键词： data communication systems

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Applications of two-photon fluorescence lifetime and second harmonic generation imaging technologies in pathological tissue sections

Applications of two-photon fluorescence lifetime and second ...

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2024 International conference on Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Li, Wei Wang, He Li, Xiaoyu Yan, Wei Wang, Yunyun Qu, Junle College of Physics and Optoelectronic Engineering Shenzhen Key Laboratory of Photonics and Biophotonics Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen University Guangdong Shenzhen518060 China Chongxin Judicial Expertise Center Hubei430415 China

ISBN: (数字)9781510680470

ISBN: (纸本)9781510680463

Two-photon fluorescence lifetime microscopy (TP-FLIM) is a powerful quantitative imaging technique that that can characterize and analyze the microenvironment changes of biological samples in the fluorescence lifetime dimension. It is not affected by factors such as excitation light intensity, sample bleaching, and fluorophore concentration, and has the advantages of high sensitivity and resolution. Therefore, it has extensive applications in biomedical research, disease diagnosis, and other fields. Second harmonic generation imaging (SHG) possesses the unique characteristics of high spatial resolution and imaging depth inherent in nonlinear optical imaging. SHG occurs in non-centrosymmetric structures, a property exhibited by collagen tissue in biological organisms. Consequently, SHG has been employed for specific tissue structure imaging. This paper combines TP-FLIM and SHG technologies to investigate pathological lung sections. Vector analysis of TP-FLIM is applied to analyze the fluorescence lifetime data of lung collagen fibers. Through TP-FLIM and SHG technologies, the lifetime of collagen fibers in the sample can be calculated and the collagen fiber tissue and non-collagen fiber tissue in the lung tissue sections can be clearly distinguished. This provides a new direction and possibility for indepth understanding of pulmonary fibrosis and other pathological mechanisms related to collagen fiber diseases, offering new insights into pathology and provisional diagnosis. © 2024 SPIE.

关键词： Harmonic generation

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Unsupervised Image Enhancement for Nonlinear Optical Microscopy with Scarce Samples

Unsupervised Image Enhancement for Nonlinear Optical Microsc...

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2023 conference on Lasers and Electro-Optics, CLEO 2023

作者： Jhang, Yun-Jie Lin, Xin Chia, Shih-Hsuan Chen, Wei-Chung Wu, I-Chen Wu, Ming-Tsang Zhuo, Guan-Yu Chen, Hung-Wen International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Institute of Biophotonics National Yang Ming Chiao Tung University Taipei11221 Taiwan Ph.D. Program in Environmental and Occupational Medicine Kaohsiung Medical University Kaohsiung80708 Taiwan Division of Gastroenterology Department of Internal Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung80708 Taiwan Department of Public Health Kaohsiung Medical University Kaohsiung80708 Taiwan Department of Family Medicine Kaohsiung Medical University Hospital Kaohsiung80708 Taiwan Institute of New Drug Development China Medical University Taichung40447 Taiwan Institute of Photonics Technologies National Tsing Hua University Hsinchu30013 Taiwan

We present an unsupervised model without any assumptions to enhance images in nonlinear optical microscopy. It only takes 30 training images and can be generalized to unseen samples. Qualitative and quantitative resul... 详细信息

ISBN: (纸本)9781957171258

关键词： Image enhancement

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