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arXiv 2021年

作者： Carrington, André M. Manuel, Douglas G. Fieguth, Paul W. Ramsay, Tim Osmani, Venet Wernly, Bernhard Bennett, Carol Hawken, Steven McInnes, Matthew Magwood, Olivia Sheikh, Yusuf Holzinger, Andreas Ottawa Hospital Research Institute Institute of Clinical Evaluative Sciences Ottawa Canada Ottawa Hospital Research Institute Department of Family Medicine School of Epidemiology and Public Health University of Ottawa Institute of Clinical Evaluative Sciences Bruyère Research Institute Canada Department of Systems Design Engineering Vision and Image Processing Lab Faculty of Engineering University of Waterloo Canada e-health group Fondazione Bruno Kessler Research Institute department of Psychology and Cognitive Science University of Trento Italy Department of Cardiology Paracelsus Medical University of Salzburg Salzburg Austria Ottawa Hospital Research Institute University of Ottawa Canada Ottawa Hospital Research Institute University of Ottawa Canada Ottawa Hospital Research Institute University of Ottawa Canada Ottawa Hospital Research Institute Institute of Clinical Evaluative Sciences Ottawa Canada Bruyere Research Institute University of Ottawa Canada Ottawa Hospital Research Institute Department of Biology University of Ottawa Canada Alberta Machine Intelligence Institute University of Alberta Canada Human-Centered AI Lab Medical University Graz Austria

Optimal performance is critical for decision-making tasks from medicine to autonomous driving, however common performance measures may be too general or too specific. For binary classifiers, diagnostic tests or prognosis at a timepoint, measures such as the area under the receiver operating characteristic curve, or the area under the precision recall curve, are too general because they include unrealistic decision thresholds. On the other hand, measures such as accuracy, sensitivity or the F1 score are measures at a single threshold that reflect an individual single probability or predicted risk, rather than a range of individuals or risk. We propose a method in between, deep ROC analysis, that examines groups of probabilities or predicted risks for more insightful analysis. We translate esoteric measures into familiar terms: AUC and the normalized concordant partial AUC are balanced average accuracy (a new finding);the normalized partial AUC is average sensitivity;and the normalized horizontal partial AUC is average specificity. Along with post-test measures, we provide a method that can improve model selection in some cases and provide interpretation and assurance for patients in each risk group. We demonstrate deep ROC analysis in two case studies and provide a toolkit in Python. © 2021, CC BY-NC-SA.

关键词： Decision making

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Investigating the automatic classification of algae using fusion of spectral and morphological characteristics of algae via deep residual learning

arXiv

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arXiv 2018年

作者： Deglint, Jason L. Jin, Chao Wong, Alexander Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada Waterloo Artificial Intelligence Institute WaterlooON Canada School of Environmental Science and Engineering Sun Yat-sen University Guangzhou510275 China

Under the impact of global climate changes and human activities, harmful algae blooms (HABs) in surface waters have become a growing concern due to negative im-pacts on water related industries, such as tourism, fishing and safe water supply. Many jurisdictions have introduced specific water quality regulations to protect public health and safety. Currently, drinking water quality guidelines related to cyanobacteria are based on maximum acceptable concentrations of toxins or elevated levels of cyanobacte-ria cells in water supplies. Therefore, reliable and cost effective methods of quantifying the type and concentration of threshold levels of algae cells has become critical for ensuring successful water management. In this work, we present SAMSON, an in-novative system to automatically classify multiple types of algae from different phyla groups by combining standard morphological features with their multi-wavelength sig-nals. Two phyla with focused investigation in this study are the Cyanophyta phylum (blue-green algae), and the Chlorophyta phylum (green algae). To accomplish this, we use a custom-designed microscopy imaging system which is configured to image water samples at two uorescent wavelengths and seven absorption wavelengths using discrete-wavelength high-powered light emitting diodes (LEDs). Powered by computer vision and machine learning, we investigate the possibility and effectiveness of auto-matic classification using a deep residual convolutional neural network. More specifically, a classification accuracy of 96% was achieved in an experiment conducted with six different algae types. This high level of accuracy was achieved using a deep resid-ual convolutional neural network that learns the optimal combination of spectral and morphological features. These findings elude to the possibility of leveraging a unique fingerprint of algae cell (i.e. spectral wavelengths and morphological features) to auto-matically distinguish different algae types. Our wo

关键词： Water supply

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Comparing LBP, HOG and Deep Features for Classification of Histopathology Images

arXiv

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arXiv 2018年

作者： Alhindi, Taha J. Kalra, Shivam Ng, Ka Hin Afrin, Anika Tizhoosh, Hamid R. Kimia Lab University of Waterloo Canada Dept. of Industrial Eng. King Abdulaziz Univ. Jeddah Saudi Arabia Systems Design Engineering University of Waterloo Canada Electrical and Computer Engineering University of Waterloo Canada

Medical image analysis has become a topic under the spotlight in recent years. There is a significant progress in medical image research concerning the usage of machine learning. However, there are still numerous questions and problems awaiting answers and solutions, respectively. In the present study, comparison of three classification models is conducted using features extracted using local binary patterns, the histogram of gradients, and a pre-trained deep network. Three common image classification methods, including support vector machines, decision trees, and artificial neural networks are used to classify feature vectors obtained by different feature extractors. We use KIMIA Path960, a publicly available dataset of 960 histopathology images extracted from 20 different tissue scans to test the accuracy of classification and feature extractions models used in the study, specifically for the histopathology images. SVM achieves the highest accuracy of 90.52% using local binary patterns as features which surpasses the accuracy obtained by deep features, namely 81.14%. Copyright © 2018, The Authors. All rights reserved.

关键词： Image classification

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SAMSON: Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae

arXiv

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arXiv 2018年

作者： Deglint, Jason L. Tang, Lyndon Wang, Yitian Jin, Chao Wong, Alexander Vision and Image Processing Lab Systems Design Engineering University of Waterloo Ontario Canada School of Environmental Science and Engineering Yat-sen University Guangzhou510275 China

This paper presents SAMSON, a Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae within a water sample. designed to be portable and low-cost for on-site use, the optical sub-system of SAMSON consists of a mixture of low-cost optics and electronics, designed specifically to capture both fluorescent and absorption responses from a water sample. The graphical user interface (GUI) sub-system of SAMSON was designed to enable flexible visualisation of algae in the water sample in real-time, with the ability to perform fine-grained exposure control and illumination wavelength selection. We demonstrate SAMSON’s capabilities by equipping the system with two fluorescent illumination sources and seven absorption illumination sources to enable the capture of multispectral data from six different algae species (three from the Cyanophyta phylum (blue-green algae) and three from the Chlorophyta phylum (green algae)). The key benefit of SAMSON is the ability to perform rapid acquisition of fluorescence and absorption data at different wavelengths and magnification levels, thus opening the door for machine learning methods to automatically identify and enumerate different algae in water samples using this rich wealth of data. Copyright © 2018, The Authors. All rights reserved.

关键词： Algae

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Effects of electrode configuration on pattern recognition based finger movement classification 19th

Effects of electrode configuration on pattern recognition ba...

引用

19th International Conference on Human-Computer Interaction, HCI International 2017

作者： He, Jiayuan Zhu, Xiangyang Jiang, Ning Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Lab of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783319587493

Pattern recognition (PR) based myoelectric control could provide intuitive and dexterous control of advanced prostheses. Previous studies showed that the performance of finger movements was not as good as that of wrist movements. As electrode configuration plays an important role in classification performance, this study investigated the effect of the number of electrodes and their locations on finger movement classification. An electrode selection algorithm, sequential forward searching (SFS), was applied on the high density (HD) electrode grid with 192 monopolar electrodes. With the time domain (TD) feature and linear discriminant analysis (LDA), it was found that the error rate was dramatically decreased with the number of electrodes increasing from one to ten. Under the optimized electrode configuration, the error rate could be lower than 10% with 8 electrodes, and 5% with 18 electrodes. The importance of the electrodes was measured and the results showed that the effective site for classification was mainly located around the flexor digitorum superficialis and extensor digitorum communis. This study provides the guideline for optimal placement of electrodes in finger movement recognition, and potentially provide sufficient controllability of advance prostheses with individually articulated fingers. © Springer International Publishing AG 2017.

关键词： Electrodes

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Genetic Algorithms for Lens design: a Review

arXiv

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arXiv 2018年

作者： Höschel, Kaspar Lakshminarayanan, Vasudevan TU Wien Karlsplatz 13 Wien1040 Austria University of Waterloo Theoretical and Experimental Epistemology Lab School of Optometry & Vision Science Depts. of Physics ECE and Systems Design Engineering WaterlooONN2L 3G1 Canada

Genetic algorithms (GAs) have a long history of over four decades. GAs are adaptive heuristic search algorithms that provide solutions for optimization and search problems. The GA derives expression from the biological terminology of natural selection, crossover, and mutation. In fact, GAs simulate the processes of natural evolution. Due to their unique simplicity, GAs are applied to the search space to find optimal solutions to various problems in science and engineering. Using GAs for lens design was investigated mostly in the 1990s, but were not fully exploited. But in the past few years there have been a number of newer studies exploring the application of GAs or hybrid GAs in optical design. In this paper we discuss the basic ideas behind GAs and demonstrate their application in optical lens design. Copyright © 2018, The Authors. All rights reserved.

关键词： Genetic algorithms

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Can a universal quantum cloner be used to design an experimentally feasible near-deterministic CNOT gate?

arXiv

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arXiv 2018年

作者： Gueddana, Amor Gholami, Peyman Lakshminarayanan, Vasudevan Theoretical and Experimental Epistemology Lab TEEL School of Optometry and Vision Science University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada Green and Smart Communication Systems Lab Gres'Com Engineering School of Communication of Tunis Sup'Com University of Carthage Ghazela Technopark Ariana2083 Tunisia Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada

We propose a non-deterministic CNOT gate based on a quantum cloner, a quantum switch based on all optical routing of single photon by single photon, a quantum-dot spin in a double-sided optical microcavity with two photonic qubits, delay lines and other linear optical photonic devices. Our CNOT provides a fidelity of 78% with directly useful outputs for a quantum computing circuit and requires no ancillary qubits or electron spin measurements. Copyright © 2018, The Authors. All rights reserved.

关键词： Particle beams

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Texture classification in extreme scale variations using GANet

arXiv

引用

arXiv 2018年

作者： Liu, Li Chen, Jie Zhao, Guoying Fieguth, Paul Chen, Xilin Pietikäinen, Matti Center for Machine Vision and Signal Analysis University of Oulu Oulu90014 Finland Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China

Research in texture recognition often concentrates on recognizing textures with intraclass variations such as illumination, rotation, viewpoint and small scale changes. In contrast, in real-world applications a change in scale can have a dramatic impact on texture appearance, to the point of changing completely from one texture category to another. As a result, texture variations due to changes in scale are amongst the hardest to handle. In this work we conduct the first study of classifying textures with extreme variations in scale. To address this issue, we first propose and then reduce scale proposals on the basis of dominant texture patterns. Motivated by the challenges posed by this problem, we propose a new GANet network where we use a Genetic Algorithm to change the units in the hidden layers during network training, in order to promote the learning of more informative semantic texture patterns. Finally, we adopt a FV-CNN (Fisher Vector pooling of a Convolutional Neural Network filter bank) feature encoder for global texture representation. Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding we are developing a new dataset, the Extreme Scale Variation Textures (ESVaT), to test the performance of our framework. It is demonstrated that the proposed framework significantly outperforms gold-standard texture features by more than 10% on ESVaT. We also test the performance of our proposed approach on the KTHTIPS2b and OS datasets and a further dataset synthetically derived from Forrest, showing superior performance compared to the state of the art. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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Skin lesion segmentation: U-Nets versus clustering

Skin lesion segmentation: U-Nets versus clustering

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Bill S. Lin Kevin Michael Shivam Kalra H.R. Tizhoosh Department of Mechanical and Mechatronics University of Waterloo Waterloo Canada Department of Systems Design Engineering University of Waterloo Waterloo Canada KIMIA Lab University of Waterloo Waterloo Canada

Many automatic skin lesion diagnosis systems use segmentation as a preprocessing step to diagnose skin conditions because skin lesion shape, border irregularity, and size can influence the likelihood of malignancy. This paper presents, examines and compares two different approaches to skin lesion segmentation. The first approach uses U-Nets and introduces a histogram equalization based preprocessing step. The second approach is a C-Means clustering based approach that is much simpler to implement and faster to execute. The Jaccard Index between the algorithm output and hand segmented images by dermatologists is used to evaluate the proposed algorithms. While many recently proposed deep neural networks to segment skin lesions require a significant amount of computational power for training (i.e., computer with GPUs), the main objective of this paper is to present methods that can be used with only a CPU. This severely limits, for example, the number of training instances that can be presented to the U-Net. Comparing the two proposed algorithms, U-Nets achieved a significantly higher Jaccard Index compared to the clustering approach. Moreover, using the histogram equalization for preprocessing step significantly improved the U-Net segmentation results.

关键词： Skin Lesions Image segmentation Training Image color analysis Neural networks Hair

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Skin Lesion Segmentation: U-Nets versus Clustering

arXiv

引用

arXiv 2017年

作者： Lin, Bill S. Michael, Kevin Kalra, Shivam Tizhoosh, H.R. Department of Mechanical and Mechatronics University of Waterloo Waterloo Canada Department of Systems Design Engineering University of Waterloo Waterloo Canada KIMIA Lab University of Waterloo Waterloo Canada

关键词： Dermatology

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