[image 01355] 医用画像・機械学習についてのセミナーご案内（8/3 奈良先端大）

[Yoshinobu Sato]

メーリングリストの皆様

セミナーの開催案内をさせていただきます。
奮ってご参加ください。

奈良先端大・佐藤嘉伸

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日本学術振興会科研費・新学術領域「多元計算解剖学」
第１１回多元計算解剖学セミナーのご案内

演題：
Patch-based Machine Learning and Deep Learning
in Medical Image Processing, Analysis and Diagnosis
（医用画像処理・解析・診断におけるパッチベース機械学習と深層学習）

演者：
Kenji Suzuki, Ph.D.
Associate Professor
Department of Electrical and Computer Engineering &
Medical Imaging Research Center
Illinois Institute of Technology
https://engineering.iit.edu/faculty/kenji-suzuki

日時：2015年8月3日（月）15:30～16:30

場所：奈良先端科学技術大学院大学・情報科学研究科・L2講義室

アクセスマップ： http://www.naist.jp/accessmap/index_j.html
キャンパスマップ： http://www.naist.jp/campusmap/index_j.html

※会場はキャンパスマップ８番の建物の１階となります。玄関を入って左方向に進んでいって右に曲がって下さい。お車でご来場の方は、キャンパスマップの「公営駐車場」（1日300円）へ駐車して下さい。正門ではなく、その上のコインパーキングゲートよりご入構ください。

※講演は日本語で行われます。参加費・事前登録不要です。

Abstract: Image processing and analysis, and computer-aids in
diagnosis are indispensable in medical imaging.  Machine leaning (ML)
has become one of the most active areas of research in the medical
imaging field, because “learning from examples or data” is crucial to
handling a large amount of data (“Big data”) coming from medical
imaging systems. Recently, as the available computational power
increased dramatically, patch/pixel-based ML emerged in the medical
imaging field, which uses pixel values in image patches directly,
instead of features calculated from segmented objects as input
information. Patch/pixel-based ML is a versatile, powerful framework
that can acquire image-processing and analysis functions, including
noise reduction, lesion and organ enhancement, pattern separation,
segmentation, and classification, through training with image
examples. On the other hand, in the computer vision field, deep
learning that has a deep architecture drew enthusiastic attentions.
Deep learning learns high-level computer-vision tasks from patches in
images, which has a close relationship with patch/pixel-based ML.  In
this talk, patch/pixel-based ML in medical image processing and
computer-aided diagnosis (CAD) of lesions in medical images is
overviewed, including separation of bones from soft tissue in chest
radiographs, radiation dose reduction in CT, lung nodule detection in
chest radiography and CT, polyp detection in CT colonography, and
detection of liver tumors in hepatic CT and MRI.  Relationships
between patch/pixel-based ML and deep learning (such as deep neural
networks) are also discussed.


問合せ先： yoshi ＠ is.naist.jp （奈良先端大　佐藤嘉伸）
「多元計算解剖学」ホームページ： http://www.tagen-compana.org/
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Yoshinobu Sato, Ph.D
Imaging-based Computational Biomedicine (ICB) Lab
Graduate School of Information Science
Nara Institute of Science and Technology (NAIST)
8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
Tel: +81(0)743-72-5230, Fax: +81(0)743-72-5239
yoshi ＠ is.naist.jp
http://isw3.naist.jp/Contents/Research/ai-05-en.html

佐藤　嘉伸
奈良先端科学技術大学院大学　情報科学研究科
〒630-0192 奈良県生駒市高山町8916-5
http://isw3.naist.jp/Contents/Research-ja/48_lab-ja.html