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DPDR: A Novel Machine Learning Method for the Decision Process for Dimensionality Reduction

SN computer Science 2024年第1期5卷 124页

作者： Dessureault, Jean-Sébastien Massicotte, Daniel LSSI ‑ Laboratory of Signal and System Integration Electrical and Computer Engineering Department Université du Québec à Trois-Rivières 3351 Bd des Forges Trois-Rivières G8Z 4M3 QC Canada

This paper discusses the critical decision process of extracting or selecting the features in a supervised learning context. It is often confusing to find a suitable method to reduce dimensionality. There are pros and cons to deciding between a feature selection and feature extraction according to the data’s nature and the user’s preferences. Indeed, the user may want to emphasize the results toward integrity or interpretability and a specific data resolution. This paper proposes a new method to choose the best dimensionality reduction method in a supervised learning context. It also helps to drop or reconstruct the features until a target resolution is reached. This target resolution can be user defined, or it can be automatically defined by the method. The method applies a regression or a classification, evaluates the results, and gives a diagnosis about the best dimensionality reduction process in this specific supervised learning context. The main algorithms used are the random forest algorithms, the principal component analysis algorithm, and the multilayer perceptron neural network algorithm. Six use cases are presented, and every one is based on some well-known technique to generate synthetic data. This research also discusses each choice that can be made in the process, aiming to clarify the issues about the entire decision process of selecting or extracting the features. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction Feature selection MLP neural network PCA algorithm Random forest algorithm

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: the next leap toward native language-based and explainable machine learning framework

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Automated Software engineering 2023年第2期30卷 1-28页

作者： Dessureault, Jean-Sébastien Massicotte, Daniel LSSI - Laboratory of Signal and System Integration Electrical and Computer Engineering Department Université du Québec á Trois-Rivières Trois-Rivières Canada

The machine learning frameworks flourished in the last decades, allowing artificial intelligence to get out of academic circles to be applied to enterprise domains. This field has significantly advanced, but there is still some meaningful improvement to reach the subsequent expectations. The proposed framework, named AI $$^{2}$$ , uses a natural language interface that allows non-specialists to benefit from machine learning algorithms without necessarily knowing how to program with a programming language. The primary contribution of the AI $$^{2}$$ framework allows a user to call the machine learning algorithms in English, making its interface usage easier. The second contribution is greenhouse gas (GHG) awareness. It has some strategies to evaluate the GHG generated by the algorithm to be called and to propose alternatives to find a solution without executing the energy-intensive algorithm. Another contribution is a preprocessing module that helps to describe and to load data properly. Using an English text-based chatbot, this module guides the user to define every dataset so that it can be described, normalized, loaded, and divided appropriately. The last contribution of this paper is about explainability. The scientific community has known that machine learning algorithms imply the famous black-box problem for decades. Traditional machine learning methods convert an input into an output without being able to justify this result. The proposed framework explains the algorithm’s process with the proper texts, graphics, and tables. The results, declined in five cases, present usage applications from the user’s English command to the explained output. Ultimately, the AI $$^{2}$$ framework represents the next leap toward native language-based, human-oriented concerns about machine learning framework.

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Multi-modal signal integration for enhanced sleep stage classification: Leveraging EOG and 2-channel EEG data with advanced feature extraction

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Artificial Intelligence in Medicine 2025年 166卷 103152-103152页

作者： Samaee, Mahdi Yazdi, Mehran Massicotte, Daniel Signal and Image Processing Laboratory School of Electrical and Computer Engineering Shiraz University Shiraz Iran Laboratory of Signal and System Integration Department of Electrical and Computer Engineering Université du Québec à Trois-Rivières Trois-Rivières Canada

This paper introduces an innovative approach to sleep stage classification, leveraging a multi-modal signal integration framework encompassing Electrooculography (EOG) and two-channel electroencephalography (EEG) data. We explore the utility of various feature extraction techniques, including Short-Time Fourier Transform (STFT), Wavelet Transform, and raw signal processing, alongside the utilization of neural networks as feature extractors. This unique combination allows us to harness the benefits of traditional feature extraction methods while capitalizing on the power of neural networks to enhance classification performance. Our comprehensive classifier evaluation encompasses a range of models, including Long Short-Term Memory (LSTM) networks and XGBoost. Remarkably, our results reveal exceptional performance with the XGBoost classifier, achieving an overall accuracy of 84.57 % and a macro-F1 score of 78.21 % on the Sleep-EDF expanded dataset, and an overall accuracy of 86.02 % and a macro-F1 score of 81.96 % on the ISRUC-Sleep dataset. Class-specific accuracies highlight its proficiency, particularly in detecting wake and N2 stages, solidifying its superiority among the classifiers tested. This amalgamation of feature sets, complemented by Principal Component Analysis (PCA) for dimensionality reduction, underscores its significance in yielding top-tier classification outcomes. The integration of traditional feature extraction methods with neural networks as feature extractors creates a robust and comprehensive system for sleep stage classification, offering the advantages of both approaches to enhance the accuracy and reliability of the results. © 2025

关键词： Discrete wavelet transforms

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Corrections to "Efficient Adaptive Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications" [W. Duan, J. Tao, and Y. Rosa Zheng, IEEE J. Ocean. Eng ., 2017, DOI: 10.1109/JOE.2017.2707285]

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IEEE Journal of Oceanic engineering 2017年第3期42卷 752-752页

作者： W. Duan J. Tao Y. R. Zheng Qualcomm Technologies Inc. San Diego CA USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

Equation In [1], (6) of the above named work is corrected.

关键词： MIMO Adaptive equalizers Underwater communication Underwater acoustics Least mean squares methods

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Self-sorting FFT method eliminating trivial multiplication and suitable for embedded DSP processor

Self-sorting FFT method eliminating trivial multiplication a...

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Annual IEEE Northeast Workshop on Circuits and systems (NEWCAS)

作者： Marwan A. Jaber Daniel Massicotte Electrical and Computer Engineering Department Laboratory of Signal and System Integrations Université du Québec à Trois-Rivières Canada

The Discrete Fourier Transform (DFT) is a mathematical procedure at the core of processing inside a Digital signal Processor. Speed and low complexity are crucial in the FFT process; they can be achieved by avoiding trivial multiplications through a proper handling of the input/output data and the twiddle factors. Accordingly, this paper presents an innovative approach for handling the input/output data efficiently by avoiding trivial multiplications. This approach consists of a simple mapping of the three indices (FFT stage, butterfly and element) to the addresses of the input/output data with their corresponding coefficient multiplier. A self-sorting algorithm that reduces the amount of memory accesses to the coefficient multipliers' memory can also reduce the computational load by avoiding all trivial multiplications. Compared with the most-recent work [5], performance evaluation in terms of the number of cycles on the general-purpose TMS320C6416 DSP shows a reduction of 29% (FFT of size 4096) and a 50% memory reduction to stock twiddle factors. The algorithm has also shown a speed gain of 24% on the FFTW platform for a FFT of size 4096.

关键词： Digital signal processing Clocks Memory management Performance evaluation Benchmark testing Discrete Fourier transforms Equations

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Interpolation of Orientation Distribution Functions (ODFs) in Q-ball Imaging

Interpolation of Orientation Distribution Functions (ODFs) i...

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Iranian Conference of Biomedical engineering

作者： Maryam Afzali Emad Fatemizadeh Hamid Soltanian-Zadeh Department of Electrical Engineering Biomedical Signal and Image Processing Laboratory(BiSIPL) Sharif University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence(CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran & Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit Michigan USA

ISBN: (纸本)9781467331289

Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive method for investigating the brain white matter structure. It can be used to evaluate fiber bundles in the brain but in the regions with crossing fibers, it fails. To resolve this problem, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding directions is used and for reconstruction, the Q-ball method is applied. In this method, orientation distribution function (ODF) of fibers can be calculated. Mathematical models play a crucial role in the field of ODF. For instance, in registering Q-ball images for applications like group analysis or atlas construction, one needs to interpolate ODFs. To this end, principal diffusion directions (PDDs) of each ODF are needed. In this paper, PDDs are defined as vectors that connect the corresponding local maxima of ODF values. Then, ODFs are interpolated using PDDs. The proposed method is evaluated and compared with previous protocols. Experimental results show that the proposed interpolation algorithm preserves the principal direction of fiber tracts without producing any deviations in the tracts. It is shown that changes in the entropy of the interpolated ODFs are almost linear and the bloating effect (blurring of the principal directions) can be removed.

关键词： High angular resolution diffusion imaging (HARDI) Q-ball imaging Orientation distribution function (ODF) Interpolation Principal diffusion direction (PDD)

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A higher radix FFT FPGA implementation suitable for OFDM systems

A higher radix FFT FPGA implementation suitable for OFDM sys...

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2011 18th IEEE International Conference on Electronics, Circuits and systems, ICECS 2011

作者： Jaber, Marwan A. Massicotte, Daniel Achouri, Youssef Université du Québec à Trois-Rivières Electrical and Computer Engineering Department Laboratory of Signal and System Integrations Canada

ISBN: (纸本)9781457718458

This article describes a new approach for higher radix butterflies suitable for pipeline implementation. Based on the butterfly computation introduced by Cooley-Tukey [1], we will introduce a novel approach for the Discrete Fourier Transform (DFT) factorization, by redefining the butterfly computation, which is more suitable for efficient VLSI implementation. The proposed factorization motivated us to present a new concept of a radix-r Fast Fourier Transform (FFT), in which the radix-r butterfly computation concept was formulated as composite engines to implement each of the butterfly computations. This concept enables the radix r butterfly-processing element (BPE) to be designed by maintaining only one complex value multiplier in the butterfly critical path for any given r [2]. Algorithmic description and performance of low complexity FFT methods are considered in this paper where the speed and accuracy evaluation of the proposed method in fixed point is also elaborated. © 2011 IEEE.

关键词： Factorization

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A higher radix FFT FPGA implementation suitable for OFDM systems

A higher radix FFT FPGA implementation suitable for OFDM sys...

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IEEE International Conference on Electronics, Circuits and systems (ICECS)

作者： Marwan A. Jaber Daniel Massicotte Youssef Achouri Electrical and Computer Engineering Department Laboratory of Signal and System Integrations Université du Québec á Trois-Rivières Canada

关键词： Discrete Fourier transforms signal processing algorithms Algorithm design and analysis Complexity theory Accuracy Delay Field programmable gate arrays

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