scispace - formally typeset
Search or ask a question
Author

Masafumi Hagiwara

Other affiliations: University of Yamanashi, NTT DATA, Stanford University  ...read more
Bio: Masafumi Hagiwara is an academic researcher from Keio University. The author has contributed to research in topics: Artificial neural network & Content-addressable memory. The author has an hindex of 29, co-authored 354 publications receiving 3174 citations. Previous affiliations of Masafumi Hagiwara include University of Yamanashi & NTT DATA.


Papers
More filters
Journal ArticleDOI
TL;DR: An Internet-based melanoma screening system that separates the tumor area from the surrounding skin using highly accurate dermatologist-like tumor area extraction algorithm, and classifies the tumor as melanoma or nevus using a neural network classifier, and presents the diagnosis.

247 citations

Proceedings ArticleDOI
08 Mar 1992
TL;DR: The author proposes extended fuzzy cognitive maps (E-FCMs) to represent causal relationships more naturally and computer simulation results indicate the effectiveness of the E- FCMs.
Abstract: Fuzzy cognitive maps (FCMs) have been proposed to represent causal reasoning by using numeric processing. They graphically represent uncertain causal reasoning. In the resonant states, there emerges a limit cycle or a hidden pattern, which is a FCM inference. However, there are some shortcomings concerned with knowledge representation in the conventional FCMs. The author proposes extended fuzzy cognitive maps (E-FCMs) to represent causal relationships more naturally. The features of the E-FCMs are nonlinear membership functions, conditional weights, and time delay weights. Computer simulation results indicate the effectiveness of the E-FCMs. >

157 citations

Proceedings Article
01 Jan 1990

111 citations

Journal ArticleDOI
TL;DR: The results indicate that the new algorithm extracted a tumour area close to that obtained by dermatologists and, in particular, the border part of the tumour was adequately extracted.
Abstract: The aims of this study were to provide a quantitative assessment of the tumour area extracted by dermatologists and to evaluate computer-based methods from dermoscopy images for refining a computer-based melanoma diagnostic system. Dermoscopic images of 188 Clark naevi, 56 Reed naevi and 75 melanomas were examined. Five dermatologists manually drew the border of each lesion with a tablet computer. The inter-observer variability was evaluated and the standard tumour area (STA) for each dermoscopy image was defined. Manual extractions by 10 non-medical individuals and by two computer-based methods were evaluated with STA-based assessment criteria: precision and recall. Our new computer-based method introduced the region-growing approach in order to yield results close to those obtained by dermatologists. The effectiveness of our extraction method with regard to diagnostic accuracy was evaluated. Two linear classifiers were built using the results of conventional and new computer-based tumour area extraction methods. The final diagnostic accuracy was evaluated by drawing the receiver operating curve (ROC) of each classifier, and the area under each ROC was evaluated. The standard deviations of the tumour area extracted by five dermatologists and 10 non-medical individuals were 8.9% and 10.7%, respectively. After assessment of the extraction results by dermatologists, the STA was defined as the area that was selected by more than two dermatologists. Dermatologists selected the melanoma area with statistically smaller divergence than that of Clark naevus or Reed naevus (P = 0.05). By contrast, non-medical individuals did not show this difference. Our new computer-based extraction algorithm showed superior performance (precision, 94.1%; recall, 95.3%) to the conventional thresholding method (precision, 99.5%; recall, 876%). These results indicate that our new algorithm extracted a tumour area close to that obtained by dermatologists and, in particular, the border part of the tumour was adequately extracted. With this refinement, the area under the ROC increased from 0.795 to 0.875 and the diagnostic accuracy showed an increase of approximately 20% in specificity when the sensitivity was 80%. It can be concluded that our computer-based tumour extraction algorithm extracted almost the same area as that obtained by dermatologists and provided improved computer-based diagnostic accuracy.

103 citations

Journal ArticleDOI
TL;DR: Extended Fuzzy Cognitive Maps are proposed to represent causal relationships more naturally and computer simulation results indicate the effectiveness of the E-FCMs.
Abstract: Fuzzy cognitive maps (FCMs) have been proposed to represent causal reasoning by using numeric processing. They graphically represent uncertain causal reasoning. In the resonant states, there emerges a limit cycle or a hidden pattern, which is a FCM inference. However, there are some shortcomings concerned with knowledge representation in the conventional FCMs. The author proposes extended fuzzy cognitive maps (E-FCMs) to represent causal relationships more naturally. The features of the E-FCMs are nonlinear membership functions, conditional weights, and time delay weights. Computer simulation results indicate the effectiveness of the E-FCMs. >

101 citations


Cited by
More filters
Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: The goal is not, in general, to replace text-based retrieval methods as they exist at the moment but to complement them with visual search tools.

1,535 citations

Journal ArticleDOI
01 Sep 2001
TL;DR: This paper features a survey of about 250 IEC research papers and discusses the IEC from the point of the future research direction of computational intelligence.
Abstract: We survey the research on interactive evolutionary computation (IEC). The IEC is an EC that optimizes systems based on subjective human evaluation. The definition and features of the IEC are first described and then followed by an overview of the IEC research. The overview primarily consists of application research and interface research. In this survey the IEC application fields include graphic arts and animation, 3D computer graphics lighting, music, editorial design, industrial design, facial image generation, speed processing and synthesis, hearing aid fitting, virtual reality, media database retrieval, data mining, image processing, control and robotics, food industry, geophysics, education, entertainment, social system, and so on. The interface research to reduce human fatigue is also included. Finally, we discuss the IEC from the point of the future research direction of computational intelligence. This paper features a survey of about 250 IEC research papers.

1,416 citations

25 Apr 2017
TL;DR: This presentation is a case study taken from the travel and holiday industry and describes the effectiveness of various techniques as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib).
Abstract: This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

1,338 citations