Kenji Suzuki | Illinois Institute of Technology (original) (raw)
Kenji Suzuki, Ph.D. (by Published Work; Nagoya University) worked at Hitachi Medical Corporation, Japan, Aichi Prefectural University, Japan, as a faculty member, and in Department of Radiology, University of Chicago, as Assistant Professor. In 2014, he joined Department of Electric and Computer Engineering and Medical Imaging Research Center, Illinois Institute of Technology, as Associate Professor. In 2017, he was jointly appointed in World Research Hub Initiative at Tokyo Institute of Technology as Professor. He published more than 320 papers (including 110 peer-reviewed journal papers). His papers were cited more than 8,000 times by other researchers. He has an h-index of 40. He is inventor on 30 patents (including 13 granted patents), which were licensed to several companies and commercialized. He published 10 books and 22 book chapters, and edited 13 journal special issues. He was awarded/co-awarded more than 25 grants as PI including NIH R01 and ACS. He served as the Editor of a number of leading international journals, including Pattern Recognition and Medical Physics. He served as a referee for 80 international journals, an organizer of 30 international conferences, and a program committee member of 150 international conferences. He received 25 awards, including 3 RSNA Certificate of Merit Awards, IEEE Outstanding Member Award, Cancer Research Foundation Young Investigator Award, University of Chicago Kurt Rossmann Award for Excellence in Teaching, IEICE 2014 Best Journal Paper Award, and Springer-Nature EANM Most Cited Journal Paper Award 2016.
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Papers by Kenji Suzuki
Knowledge-Based Systems, 2016
Feature subset selection (FSS) has been an active area of research in machine learning. A number ... more Feature subset selection (FSS) has been an active area of research in machine learning. A number of techniques have been developed for selecting an optimal or sub-optimal subset of features, because it is a major factor to determine the performance of a machine-learning technique. In this paper, we propose and develop a novel optimization technique, namely, a binary coordinate ascent (BCA) algorithm that is an iterative deterministic local optimization that can be coupled with wrapper or filter FSS. The algorithm searches throughout the space of binary coded input variables by iteratively optimizing the objective function in each dimension at a time. We investigated our BCA approach in wrapper-based FSS under area under the receiver-operating-characteristic (ROC) curve (AUC) criterion for the best subset of features in classification. We evaluated our BCA-based FSS in optimization of features for support vector machine, multilayer perceptron, and Naïve Bayes classifiers with 12 datasets. Our experimental datasets are distinct in terms of the number of attributes (ranging from 18 to 11,340), and the number of classes (binary or multi-class classification). The efficiency in terms of the number of subset evaluations was improved substantially (by factors of 5-37) compared with two popular FSS meta-heuristics, i.e., sequential forward selection (SFS) and sequential floating forward selection (SFFS), while the classification performance for unseen data was maintained.
Background: Current measurement of the single longest dimension of a polyp is subjective and has ... more Background: Current measurement of the single longest dimension of a polyp is subjective and has variations among radiologists. Our purpose was to develop a computerized measurement of polyp volume in computed tomography colonography (CTC).
Methods: We developed a 3D automated scheme for measuring polyp volume at CTC. Our scheme consisted of segmentation of colon wall to confine polyp segmentation to the colon wall, extraction of a highly polyp-like seed region based on the Hessian matrix, a 3D volume growing technique under the minimum surface expansion criterion for segmentation of polyps, and sub-voxel refinement and surface smoothing for obtaining a smooth polyp surface. Our database consisted of 30 polyp views (15 polyps) in CTC scans from 13 patients. Each patient was scanned in the supine and prone positions. Polyp sizes measured in optical colonoscopy (OC) ranged from 6-18 mm with a mean of 10 mm. A radiologist outlined polyps in each slice and calculated volumes by summation of volumes in each slice. The measurement study was repeated 3 times at least 1 week apart for minimizing a memory effect bias. We used the mean volume of the three studies as “gold standard”.
Results: Our measurement scheme yielded a mean polyp volume of 0.38 cc (range, 0.15-1.24 cc), whereas a mean “gold standard” manual volume was 0.40 cc (range, 0.15-1.08 cc). The “gold-standard” manual and computer volumetric reached excellent agreement (intra-class correlation coefficient =0.80), with no statistically significant difference [P (F≤f) =0.42].
Conclusions: We developed an automated scheme for measuring polyp volume at CTC based on Hessian matrix-based shape extraction and volume growing. Polyp volumes obtained by our automated scheme agreed excellently with “gold standard” manual volumes. Our fully automated scheme can efficiently provide accurate polyp volumes for radiologists; thus, it would help radiologists improve the accuracy and efficiency of polyp volume measurements in CTC.
Books by Kenji Suzuki
Artificial neural networks may probably be the single most successful technology in the last two ... more Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of architectures, methodologies, and applications of artificial neural networks. The book consists of two parts: the architecture part covers architectures, design, optimization, and analysis of artificial neural networks; the applications part covers applications of artificial neural networks in a wide range of areas including biomedical, industrial, physics, and financial applications. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks. The target audience of this book includes college and graduate students, and engineers in companies.
Artificial neural networks may probably be the single most successful technology in the last two ... more Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of artificial neural networks in industrial and control engineering applications. The book begins with a review of applications of artificial neural networks in textile industries. Particular applications in textile industries follow. Parts continue with applications in materials science and industry such as material identification, and estimation of material property and state, food industry such as meat, electric and power industry such as batteries and power systems, mechanical engineering such as engines and machines, and control and robotic engineering such as system control and identification, fault diagnosis systems, and robot manipulation. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in industrial and control engineering areas. The target audience includes professors and students in engineering schools, and researchers and engineers in industries.
Artificial neural networks may probably be the single most successful technology in the last two ... more Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. The purpose of this book is to provide recent advances of artificial neural networks in biomedical applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. Advanced architectures for biomedical applications, which offer improved performance and desirable properties, follow. Parts continue with biological applications such as gene, plant biology, and stem cell, medical applications such as skin diseases, sclerosis, anesthesia, and physiotherapy, and clinical and other applications such as clinical outcome, telecare, and pre-med student failure prediction. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in biomedical areas. The target audience includes professors and students in engineering and medical schools, researchers and engineers in biomedical industries, medical doctors, and healthcare professionals.
Knowledge-Based Systems, 2016
Feature subset selection (FSS) has been an active area of research in machine learning. A number ... more Feature subset selection (FSS) has been an active area of research in machine learning. A number of techniques have been developed for selecting an optimal or sub-optimal subset of features, because it is a major factor to determine the performance of a machine-learning technique. In this paper, we propose and develop a novel optimization technique, namely, a binary coordinate ascent (BCA) algorithm that is an iterative deterministic local optimization that can be coupled with wrapper or filter FSS. The algorithm searches throughout the space of binary coded input variables by iteratively optimizing the objective function in each dimension at a time. We investigated our BCA approach in wrapper-based FSS under area under the receiver-operating-characteristic (ROC) curve (AUC) criterion for the best subset of features in classification. We evaluated our BCA-based FSS in optimization of features for support vector machine, multilayer perceptron, and Naïve Bayes classifiers with 12 datasets. Our experimental datasets are distinct in terms of the number of attributes (ranging from 18 to 11,340), and the number of classes (binary or multi-class classification). The efficiency in terms of the number of subset evaluations was improved substantially (by factors of 5-37) compared with two popular FSS meta-heuristics, i.e., sequential forward selection (SFS) and sequential floating forward selection (SFFS), while the classification performance for unseen data was maintained.
Background: Current measurement of the single longest dimension of a polyp is subjective and has ... more Background: Current measurement of the single longest dimension of a polyp is subjective and has variations among radiologists. Our purpose was to develop a computerized measurement of polyp volume in computed tomography colonography (CTC).
Methods: We developed a 3D automated scheme for measuring polyp volume at CTC. Our scheme consisted of segmentation of colon wall to confine polyp segmentation to the colon wall, extraction of a highly polyp-like seed region based on the Hessian matrix, a 3D volume growing technique under the minimum surface expansion criterion for segmentation of polyps, and sub-voxel refinement and surface smoothing for obtaining a smooth polyp surface. Our database consisted of 30 polyp views (15 polyps) in CTC scans from 13 patients. Each patient was scanned in the supine and prone positions. Polyp sizes measured in optical colonoscopy (OC) ranged from 6-18 mm with a mean of 10 mm. A radiologist outlined polyps in each slice and calculated volumes by summation of volumes in each slice. The measurement study was repeated 3 times at least 1 week apart for minimizing a memory effect bias. We used the mean volume of the three studies as “gold standard”.
Results: Our measurement scheme yielded a mean polyp volume of 0.38 cc (range, 0.15-1.24 cc), whereas a mean “gold standard” manual volume was 0.40 cc (range, 0.15-1.08 cc). The “gold-standard” manual and computer volumetric reached excellent agreement (intra-class correlation coefficient =0.80), with no statistically significant difference [P (F≤f) =0.42].
Conclusions: We developed an automated scheme for measuring polyp volume at CTC based on Hessian matrix-based shape extraction and volume growing. Polyp volumes obtained by our automated scheme agreed excellently with “gold standard” manual volumes. Our fully automated scheme can efficiently provide accurate polyp volumes for radiologists; thus, it would help radiologists improve the accuracy and efficiency of polyp volume measurements in CTC.
Artificial neural networks may probably be the single most successful technology in the last two ... more Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of architectures, methodologies, and applications of artificial neural networks. The book consists of two parts: the architecture part covers architectures, design, optimization, and analysis of artificial neural networks; the applications part covers applications of artificial neural networks in a wide range of areas including biomedical, industrial, physics, and financial applications. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks. The target audience of this book includes college and graduate students, and engineers in companies.
Artificial neural networks may probably be the single most successful technology in the last two ... more Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of artificial neural networks in industrial and control engineering applications. The book begins with a review of applications of artificial neural networks in textile industries. Particular applications in textile industries follow. Parts continue with applications in materials science and industry such as material identification, and estimation of material property and state, food industry such as meat, electric and power industry such as batteries and power systems, mechanical engineering such as engines and machines, and control and robotic engineering such as system control and identification, fault diagnosis systems, and robot manipulation. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in industrial and control engineering areas. The target audience includes professors and students in engineering schools, and researchers and engineers in industries.
Artificial neural networks may probably be the single most successful technology in the last two ... more Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. The purpose of this book is to provide recent advances of artificial neural networks in biomedical applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. Advanced architectures for biomedical applications, which offer improved performance and desirable properties, follow. Parts continue with biological applications such as gene, plant biology, and stem cell, medical applications such as skin diseases, sclerosis, anesthesia, and physiotherapy, and clinical and other applications such as clinical outcome, telecare, and pre-med student failure prediction. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in biomedical areas. The target audience includes professors and students in engineering and medical schools, researchers and engineers in biomedical industries, medical doctors, and healthcare professionals.