An Introduction to Deep Learing in Image Recognition （3） AutoEncoder and Anomaly Detection

HARA Takeshi, MATSUSAKO Masaki

doi:10.11409/mit.39.189

Bibliographic Information

Other Title

深層学習による画像認識入門（3）自己符号化器と異常検知

Abstract

<p>Deep learning has realized image classification methods by a data-driven mechanism. It can be said that the relationship between the input data and the correct answer was learned from a large amount of data. When tackling an image classification problem using this principle, it is necessary to collect a large amount of data on rare events in order to deal with rare events, but this can be an impossible task in reality. Anomaly detection is a unique approach that realizes the separation of rare events based on an idea of defining the range of normal features using a large amount of normal data and determining the degree of abnormality by deviation from it. The image feature extraction method using an autoencoder and its evaluation method was described in this course.</p>

Journal

Medical Imaging Technology

Medical Imaging Technology 39 (4), 189-194, 2021-09-25

The Japanese Society of Medical Imaging Technology

Keywords

Details 詳細情報について

CRID: 1390290072658190080

NII Article ID: 130008116949

DOI: 10.11409/mit.39.189

ISSN: 21853193; 0288450X

Text Lang: ja

Data Source

JaLC
CiNii Articles
KAKEN

Abstract License Flag: Disallowed

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