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4 results on '"Yusuke Sugomori"'

1. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach

Author

Hiroki Matsuzaki, Hirohisa Miura, Tatsuya Oda, Masahiko Watanabe, Takahiro Yamanashi, Daichi Kitaguchi, Hiroaki Takano, Yusuke Sugomori, Tsuyoshi Enomoto, Nobuyoshi Takeshita, Masaaki Ito, Seigo Hara, Yohei Owada, and Daisuke Sato

Subjects
Operative Time, Phase (waves), 030230 surgery, Convolutional neural network, Workflow, Set (abstract data type), 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Colon, Sigmoid, Computer Systems, Humans, Medicine, Colectomy, Retrospective Studies, business.industry, Deep learning, Surgical action, Frame (networking), Pattern recognition, Surgery, Computer-Assisted, Laparoscopy, 030211 gastroenterology & hepatology, Surgery, Laparoscopic sigmoidectomy, Artificial intelligence, business, Test data
Abstract

Automatic surgical workflow recognition is a key component for developing the context-aware computer-assisted surgery (CA-CAS) systems. However, automatic surgical phase recognition focused on colorectal surgery has not been reported. We aimed to develop a deep learning model for automatic surgical phase recognition based on laparoscopic sigmoidectomy (Lap-S) videos, which could be used for real-time phase recognition, and to clarify the accuracies of the automatic surgical phase and action recognitions using visual information. The dataset used contained 71 cases of Lap-S. The video data were divided into frame units every 1/30 s as static images. Every Lap-S video was manually divided into 11 surgical phases (Phases 0–10) and manually annotated for each surgical action on every frame. The model was generated based on the training data. Validation of the model was performed on a set of unseen test data. Convolutional neural network (CNN)-based deep learning was also used. The average surgical time was 175 min (± 43 min SD), with the individual surgical phases also showing high variations in the duration between cases. Each surgery started in the first phase (Phase 0) and ended in the last phase (Phase 10), and phase transitions occurred 14 (± 2 SD) times per procedure on an average. The accuracy of the automatic surgical phase recognition was 91.9% and those for the automatic surgical action recognition of extracorporeal action and irrigation were 89.4% and 82.5%, respectively. Moreover, this system could perform real-time automatic surgical phase recognition at 32 fps. The CNN-based deep learning approach enabled the recognition of surgical phases and actions in 71 Lap-S cases based on manually annotated data. This system could perform automatic surgical phase recognition and automatic target surgical action recognition with high accuracy. Moreover, this study showed the feasibility of real-time automatic surgical phase recognition with high frame rate.

Published
2019

2. Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study

Author

Yusuke Sugomori, Koichiro Yasaka, Seigo Hara, Yasuhiro Nakata, Kuni Ohtomo, Shigeru Kiryu, Osamu Abe, Maria Seo, and Hiroyuki Akai

Subjects
Adult, Male, medicine.medical_specialty, media_common.quotation_subject, Audiology, Overfitting, Convolutional neural network, Proof of Concept Study, 030218 nuclear medicine & medical imaging, Diagnosis, Differential, 03 medical and health sciences, 0302 clinical medicine, Deep Learning, Parkinsonian Disorders, parasitic diseases, Medicine, Humans, Radiology, Nuclear Medicine and imaging, Medical diagnosis, Normality, media_common, Aged, Retrospective Studies, Aged, 80 and over, Receiver operating characteristic, business.industry, Deep learning, Parkinson Disease, General Medicine, Middle Aged, Multiple System Atrophy, Magnetic Resonance Imaging, Data set, ROC Curve, 030220 oncology & carcinogenesis, Female, Artificial intelligence, Radiology, Neural Networks, Computer, Supranuclear Palsy, Progressive, Differential diagnosis, business
Abstract

To evaluate the diagnostic performance of deep learning with the convolutional neural networks (CNN) to distinguish each representative parkinsonian disorder using MRI. This clinical retrospective study was approved by the institutional review board, and the requirement for written informed consent was waived. Midsagittal T1-weighted MRI of a total of 419 subjects (125 Parkinson’s disease (PD), 98 progressive supranuclear palsy (PSP), and 54 multiple system atrophy with predominant parkinsonian features (MSA-P) patients, and 142 normal subjects) between January 2012 and April 2016 was retrospectively assessed. To deal with the overfitting problem of deep learning, all subjects were randomly divided into training (85%) and validation (15%) data sets with the same proportions of each disease and normal subjects. We trained the CNN to distinguish each parkinsonian disorder using single midsagittal T1-weighted MRI with a training group to minimize the differences between predicted output probabilities and the clinical diagnoses; then, we adopted the trained CNN to the validation data set. Subjects were classified into each parkinsonian disorder or normal condition according to the final diagnosis of the trained CNN, and we assessed the diagnostic performance of the CNN. The accuracies of diagnostic performances regarding PD, PSP, MSA-P, and normal subjects were 96.8, 93.7, 95.2, and 98.4%, respectively. The areas under the receiver operating characteristic curves for distinguishing each condition from others (PD, PSP, MSA-P, and normal subjects) were 0.995, 0.982, 0.990, and 1.000, respectively. Deep learning with CNN enables highly accurate discrimination of parkinsonian disorders using MRI. • Deep learning convolution neural network achieves differential diagnosis of PD, PSP, MSA-P, and normal controls with an accuracy of 96.8, 93.7, 95.2, and 98.4%, respectively. • The areas under the curves for distinguishing between PD, PSP, MSA-P, and normality were 0.995, 0.982, 0.990, and 1.000, respectively. • CNN may learn important features that humans not notice, and has a possibility to perform previously impossible diagnoses.

Published
2019

3. Deep Learning: Practical Neural Networks with Java

Author

Yusuke Sugomori, Bostjan Kaluza, Fabio M. Soares, Alan M. F. Souza, Yusuke Sugomori, Bostjan Kaluza, Fabio M. Soares, and Alan M. F. Souza

Subjects
Machine learning, Java (Computer program language)
Abstract

About This BookDevelop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries.Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applicationsThis step-by-step guide will help you solve real-world problems and links neural network theory to their applicationWho This Book Is ForThis course is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life.What You Will LearnGet a practical deep dive into machine learning and deep learning algorithmsExplore neural networks using some of the most popular Deep Learning frameworksDive into Deep Belief Nets and Stacked Denoising Autoencoders algorithmsApply machine learning to fraud, anomaly, and outlier detectionExperiment with deep learning concepts, algorithms, and the toolbox for deep learningSelect and split data sets into training, test, and validation, and explore validation strategiesApply the code generated in practical examples, including weather forecasting and pattern recognitionIn DetailMachine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognitionStarting with an introduction to basic machine learning algorithms, this course takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. This course helps you solve challenging problems in image processing, speech recognition, language modeling. You will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text. You will also work with examples such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning and more. By the end of this course, you will have all the knowledge you need to perform deep learning on your system with varying complexity levels, to apply them to your daily work.The course provides you with highly practical content explaining deep learning with Java, from the following Packt books:Java Deep Learning EssentialsMachine Learning in JavaNeural Network Programming with Java, Second Edition

Published
2017

4. Java Deep Learning Essentials

Author

Yusuke Sugomori and Yusuke Sugomori

Subjects
Application software--Development, Java (Computer program language), Machine learning
Abstract

About This BookGo beyond the theory and put Deep Learning into practice with JavaFind out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and CaffeWhether you're a data scientist or Java developer, dive in and find out how to tackle Deep LearningWho This Book Is ForThis book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects.What You Will LearnGain a practical understanding of machine learning and deep learning algorithmsImplement machine learning algorithms related to deep learningExplore neural networks using some of the most popular deep learning frameworksDive into Deep Belief Nets and Stacked Denoising Autoencoder algorithmsDiscover more deep learning algorithms with Dropout and Convolutional Neural NetworksGain an insight into the deep learning library DL4J and its practical usesGet to know device strategies in order to use deep learning algorithms and libraries in the real worldExplore deep learning further with Theano, TensorFlow, and CaffeIn DetailAI and deep learning are transforming the way we understand software, making computers more intelligent. Deep learning algorithms are being used across a broad range of industries, moving beyond the realm of data science.Starting with an introduction to basic machine learning algorithms, Java Deep Learning Essentials takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. You'll then start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and solve challenging problems in image processing, speech recognition, and language modeling. You'll also get to grips with Theano, TensorFlow, and Caffe, two of the most important tools in deep learning today. By the end of the book, you'll be ready to tackle deep learning with Java and become a part of the deep learning revolution!

Published
2016

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