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Measuring Perceptual Color Differences of Smartphone Photographs

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arXiv 2022年

作者： Wang, Zhihua Xu, Keshuo Yang, Yang Dong, Jianlei Gu, Shuhang Xu, Lihao Fang, Yuming Ma, Kede The Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China The Department of Computer Science City University of Hong Kong Kowloon Hong Kong The School of Information Management Jiangxi University of Finance and Economics Nanchang China The Guangdong OPPO Mobile Telecommunications Corp. Ltd. China The School of Computer Science and Engineering University of Electronic Science and Technology China The School of Digital Media and Art Design Hangzhou Dianzi University Hangzhou China

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of smartphone photography characterized by greater content complexities and learning-based image signal processors. In this paper, we put together so far the largest image dataset for perceptual CD assessment, in which the photographic images are 1) captured by six flagship smartphones, 2) altered by Photoshop®, 3) post-processed by built-in filters of the smartphones, and 4) reproduced with incorrect color profiles. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30, 000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data, and empirically behaves as a proper metric in the mathematical sense. Our dataset and code are publicly available at https://***/hellooks/CDNet. © 2022, CC BY.

关键词： Smartphones

Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging

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arXiv 2023年

作者： Dorosti, Tina Schultheiss, Manuel Hofmann, Felix Thalhammer, Johannes Kirchner, Luisa Urban, Theresa Pfeiffer, Franz Schaff, Florian Lasser, Tobias Pfeiffer, Daniela Chair of Biomedical Physics Department of Physics School of Natural Sciences Technical University of Munich Bavaria Garching85748 Germany Munich Institute of Biomedical Engineering Technical University of Munich Bavaria Garching85748 Germany Department of Diagnostic and Interventional Radiology School of Medicine and Health Klinikum rechts der Isar Technical University of Munich Bavaria Munich81675 Germany Institute for Advanced Study Technical University of Munich Bavaria Garching85748 Germany Computational Imaging and Inverse Problems Department of Computer Science School of Computation Information and Technology Technical University of Munich Bavaria Garching85748 Germany

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7,194 contrast-enhanced CT images (3,597 with COPD;3,597 healthy controls) from 78 subjects were selected retrospectively (01.2018-12.2021) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline ‘full-range’ window setting. Class-balanced train, validation, and test sets contained 3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower,upper limit 95% confidence] was utilized to compare model variations. Repeated inference (n=7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89]. By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range. © 2023, CC BY.

关键词： Convolutional neural networks

Forecasting models for coronavirus (COVID-19): A survey of the state-of-the-art

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TechRxiv

TechRxiv 2020年

作者： Mahalle, Parikshit N. Kalamkar, Asmita B. Dey, Nilanjan Chaki, Jyotismita Hassanien, Aboul ella Shinde, Gitanjali R. Department of Computer Engineering Sinhgad Institutes's Smt. Kashibai Navale College of Engineering Maharashtra Pune India Department of Communication Media and Information Technologies Aalborg University Copenhagen Denmark Dept. of Information Technology Techno International New Town Kolkata India School of Information Technology and Engineering Vellore Institute of Technology Vellore India Faculty of Computers & Information Information Technology Department Cairo University Egypt

Coronavirus (COVID-19) is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Governments all over the world have been forced to take critical and tough decisions as step to contain the spread of the disease. Forecasting techniques can be inculcated thereby assisting the Government in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions will help governments all over the world to prepare for the forthcoming situations. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory/mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study two categories of datasets have been discussed i.e. big data accessed from WHO/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people and the governments that are currently fighting the global COVID-19 pandemic. © 2020, CC BY.

关键词： COVID-19

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning

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arXiv 2021年

作者： Mohammad Forkana, Abdur Rahim Kang, Yong-Bin Jayaraman, Prem Prakash Liao, Kewen Kaul, Rohit Morgan, Graham Ranjan, Rajiv Sinha, Samir Department of Computer Science and Software Engineering Swinburne University of Technology MelbourneVIC Australia Department of Media and Communication Swinburne University of Technology MelbourneVIC Australia Discipline of Information Technology Peter Faber Business School Australian Catholic University SydneyNSW Australia Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Robonomics AI York Street SydneyNSW Australia

In this paper, we propose a new technique that applies automated image analysis in the area of structural corrosion monitoring and demonstrate improved efficacy compared to existing approaches. Structural corrosion monitoring is the initial step of the risk-based maintenance philosophy and depends on an engineer’s assessment regarding the risk of building failure balanced against the fiscal cost of maintenance. This introduces the opportunity for human error which is further complicated when restricted to assessment using drone captured images for those areas not reachable by humans due to many background noises. The importance of this problem has promoted an active research community aiming to support the engineer through the use of artificial intelligence (AI) image analysis for corrosion detection. In this paper, we advance this area of research with the development of a framework, CorrDetector. CorrDetector uses a novel ensemble deep learning approach underpinned by convolutional neural networks (CNNs) for structural identification and corrosion feature extraction. We provide an empirical evaluation using real-world images of a complicated structure (e.g. telecommunication tower) captured by drones, a typical scenario for engineers. Our study demonstrates that the ensemble approach of CorrDetector significantly outperforms the state-of-the-art in terms of classification accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

Lung Nodule Detection and Classification using Random Forest: A Review 9

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Lung Nodule Detection and Classification using Random Forest...

9th IEEE International Conference on Intelligent Computing and information Systems, ICICIS 2019

作者： El-Askary, Nada S. Salem, Mohammed A.-M. Roushdy, Mohamed I. Faculty of Computer and Information Sciences Ain Shams University Computer Science Department Cairo Egypt Faculty of Media Engineering and Technology German University Cairo Egypt Faculty of Computer and Information Sciences Future University in Egypt Egypt

ISBN: (纸本)9781728139951

Lung nodule is an abnormal growth of tissues in the lung that can be an onset for lung cancer. Fast detection for those nodules and classifying them will ensure better chances for treatments. Random Forest (RF) is a powerful machine learning algorithm and a state-of-the-art technology that proved to give rewarding results in helping radiologies diagnosing lung pathologies. The paper presents a survey on recent researches made for lung nodule detection and classification using RF. Wide range of datasets can be used for lung nodule detection are listed. Different models with the used features and their results are discussed in this review. © 2019 IEEE.

关键词： Surveys

Highly Efficient THz Waves Using Laser Chaos

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Highly Efficient THz Waves Using Laser Chaos

International Conference on Infrared and Millimeter Waves

作者： Fumiyoshi Kuwashima Mona Jarrahi Semih Cakmakyapan Osamu Morikawa Takuya Shirao Kazuyuki Iwao Kazuyoshi Kurihara Hideaki Kitahara Takeshi Furuya Kenji Wada Yuki Kawakami Takeshi Moriyasu Makoto Nakajima Masahiko Tani Fukui University of Technology 3-6-1 Gakuen Fukui Japan Electrical and Computer Engineering Department University of California Los Angeles 66-147E Engineering IV 420 Westwood Plaza Los Angeles CA USA Chair of Liberal Arts Japan Coast Guard Academy Wakabacho 5-1 Kure Hiroshima Japan School of Education. University. of Fukui 3-9-1 Bunkyo Fukui Japan Research Center for Development of Far-Infrared Region University of Fukui 3-9-1 Bunkyo Fukui Japan Department of Physics and Electronics Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Sakai Osaka Japan Department of Electronics and Information Engineering National Institute of Technology (KOSEN) Fukui College Geshi-Cho Sabae Fukui Japan Faculty of Engineering University of Fukui 3-9-1 Bunkyo University of Fukui Fukui-city Fukui Japan Institute of Laser engineering Osaka Univ. 2-6 Yamadaoka Suita Osaka Japan

Efficiency of optical beats in a chaotically oscillating laser diode is confirmed comparing that of free running CW laser using a highly efficient plasmonic photomixer. The great potential of chaotically oscillating l...

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AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks

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arXiv 2021年

作者： Roy, Swalpa Kumar Paoletti, Mercedes E. Haut, Juan M. Dubey, Shiv Ram Kar, Purbayan Plaza, Antonio Chaudhuri, Bidyut B. The Computer Science and Engineering Alipurduar Government Engineering and Management College 736206 India The Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Cáceres10003 Spain The Computer Vision and Biometrics Lab Indian Institute of Information Technology Prayagraj Uttar Pradesh Allahabad211015 India The Media Analysis Group Sony Research India Private Limited Karnataka Bangalore560103 India The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Convolutional neural networks (CNNs) are trained using stochastic gradient descent (SGD)-based optimizers. Recently, the adaptive moment estimation (Adam) optimizer has become very popular due to its adaptive momentum, which tackles the dying gradient problem of SGD. Nevertheless, existing optimizers are still unable to exploit the optimization curvature information efficiently. This paper proposes a new AngularGrad optimizer that considers the behavior of the direction/angle of consecutive gradients. This is the first attempt in the literature to exploit the gradient angular information apart from its magnitude. The proposed AngularGrad generates a score to control the step size based on the gradient angular information of previous iterations. Thus, the optimization steps become smoother as a more accurate step size of immediate past gradients is captured through the angular information. Two variants of AngularGrad are developed based on the use of Tangent or Cosine functions for computing the gradient angular information. Theoretically, AngularGrad exhibits the same regret bound as Adam for convergence purposes. Nevertheless, extensive experiments conducted on benchmark data sets against state-of-the-art methods reveal a superior performance of AngularGrad. Source code: https://***/mhaut/AngularGrad. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

Reusable Data Visualization Patterns for Clinical Practice 1st

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Reusable Data Visualization Patterns for Clinical Practice

1st International Conference on Systems Modelling and Management, ICSMM 2020

作者： Rabbi, Fazle Wake, Jo Dugstad Nordgreen, Tine Department of Information Science and Media Studies University of Bergen Bergen Norway Department of Computer Technology Electrical Engineering and Science Western Norway University of Applied Sciences Bergen Norway NORCE Norwegian Research Centre Bergen Norway eMeistring Bjørgvin DPS Division of Psychiatry Haukeland University Hospital Bergen Norway Department of Clinical Psychology University of Bergen Bergen Norway

ISBN: (纸本)9783030581664

Among clinical psychologists involved in guided internet-facilitated interventions, there is an overarching need to understand patients symptom development and learn about patients need for treatment support. Data visualizations is a technique for managing enormous amounts of data and extract useful information, and is often used in developing digital tool support for decision-making. Although there exists numerous data visualisation and analytical reasoning techniques available through interactive visual interfaces, it is a challenge to develop visualizations that are relevant and suitable in a healthcare context, and can be used in clinical practice in a meaningful way. For this purpose it is necessary to identify actual needs of healthcare professionals and develop reusable data visualization components according to these needs. In this paper we present a study of decision support needs of psychologists involved in online internet-facilitated cognitive behavioural therapy. Based on these needs, we provide a library of reusable visual components using a model-based approach. The visual components are featured with mechanisms for investigating data using various levels of abstraction and causal analysis. © 2020, Springer Nature Switzerland AG.

关键词： Data visualization