| Academic Year |
2024Year |
School/Graduate School |
School of Integrated Arts and Sciences Department of Integrated Arts and Sciences |
| Lecture Code |
ANM23001 |
Subject Classification |
Specialized Education |
| Subject Name |
情報数理学特講ＩＩ |
Subject Name
（Katakana） |
ジョウホウスウリガクトッコウII |
Subject Name in
English |
Topics in Mathematical and Information Sciences II |
| Instructor |
ISHIKAWA MASAHIRO |
Instructor
(Katakana) |
イシカワ マサヒロ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, First Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte：IAS J201 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Lecture |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
J
:
Japanese |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
Third/ Fourth Grade students in Faculty of Integrated Arts and Sciences |
| Keywords |
Medical image processing and its applications, Medical image processing, Pathological image processing, Computer-aided diagnosis, Deep learning, Machine learning |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | Integrated Arts and Sciences
（Knowledge and Understanding）
・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework.
（Abilities and Skills）
・The ability and skills to specify necessary theories and methods for consideration of issues. |
|---|
Class Objectives
/Class Outline |
This course aims to provide students with an understanding and mastery of the primary and applied technologies in image processing, especially focusing on medical image processing and its applications, among the image processing technologies currently the focus of attention. Adversarial Networks, Image Processing, Artificial Intelligence, Spectral Transmission, Image Processing, Medical Image Processing, DICOM Images, Machine Learning, etc.
Translated with DeepL.com (free version) |
| Class Schedule |
#1
Computer Aided Diagnosis
#2
Pathological Image Processing
#3
Hyperspectral Imaging
#4
MRI
#5
Renal Function Estimation
#6
Deep Learning
#7
Generative Adversarial Networks
#8
Image Processing
#9
Artificial Intelligence
#10
Spectral Transmission
#11
Spectral Transmission, Image Processing, Medical Image Processing, DICOM Images, Machine Learning
#12
Medical Image Processing
#13
Medical Image Processing II
#14
DICOM Images, Machine Learning
#15
DICOM Images, Machine Learning II
Post-class Report |
Text/Reference
Books,etc. |
１．鳥脇純一郎、「３次元ディジタル画像処理」、昭晃堂、2003年3月10日．
２．日本デジタルパソロジー研究会、「デジタルパソロジー入門」、篠原出版新社、2017年9月19日．
３．富野康日己、柏原直樹、成田一衝、「Annual Review 2016 腎臓」、中外医学社、2016年．
４．Nikhil Budubuduma, 「実践Deep Learning」オライリージャパン、2018年4月24日． |
PC or AV used in
Class,etc. |
|
| (More Details) |
PC-necessary |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Preparation: Read the handouts distributed in advance
Review: Summarize important points learned in the class. |
| Requirements |
|
| Grading Method |
Mini test (40%), Post-class Report (60%) |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
Schedule
Scheduled for 5 days between Sept. 2 and Sept. 10
This lecture is conducted from September 2nd to September 6th.
Date and Time
September 2nd, period 3-8
September 3rd, period 3-8
September 4th, period 3-8
September 5th, period 3-8
September 6th, period 3-8 |
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |