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Applications of Neural Networks in Medicine (5 cr)

Code: TX00EY18-3001

General information


Enrollment
06.05.2024 - 20.10.2024
Registration for the implementation has ended.
Timing
21.10.2024 - 15.12.2024
Implementation has ended.
Number of ECTS credits allocated
5 cr
Local portion
5 cr
Mode of delivery
On-campus
Unit
(2019-2024) School of ICT
Campus
Karaportti 2
Teaching languages
Finnish
Seats
0 - 35
Degree programmes
Information and Communication Technology

Implementation has 16 reservations. Total duration of reservations is 48 h 0 min.

Time Topic Location
Mon 21.10.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5010 Digitila
Thu 24.10.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPB6005 Oppimistila
Mon 28.10.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5010 Digitila
Thu 31.10.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPC5014 Oppimistila
Mon 04.11.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5010 Digitila
Thu 07.11.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5026 Luentosali
Mon 11.11.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5010 Digitila
Thu 14.11.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5026 Luentosali
Mon 18.11.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5010 Digitila
Thu 21.11.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5026 Luentosali
Mon 25.11.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5010 Digitila
Thu 28.11.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5026 Luentosali
Mon 02.12.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5010 Digitila
Thu 05.12.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5026 Luentosali
Mon 09.12.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
KMD751 Oppimistila
Thu 12.12.2024 time 09:00 - 12:00
(3 h 0 min)
Neuroverkkojen sovellukset lääketieteessä TX00EY18-3001
MPA5026 Luentosali
Changes to reservations may be possible.

Objective

After completing the course, the student understands the basics of neural network operation, is able to create neural network models implemented in Python for medical applications (including the classification of medical images and natural language processing) and knows the basics of using software as a medical device.

Content

Software as a medical device
Fundamentals of neural networks, convolutional and feedback neural networks
Medical applications
Python programming
Ethical aspects of technology application and liability issues

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

Student has achieved the course objectives. Student will be able to identify, define and use the course subject area’s concepts and models. Student understands the criteria and principles of the expertise development.

Assessment criteria, good (3)

Student has achieved the course objectives well, even though the knowledge and skills need improvement on some areas. Student is able to define the course concepts and models and is able to justify the analysis. Student is able to apply his/her knowledge in study and work situations. Student understands the importance of expertise in the field of information and communication technology and is able to analyze his/her own expertise.

Assessment criteria, excellent (5)

Student has achieved the objectives of the course with excellent marks. Student masters commendably the course subject area’s concepts and models. Student is able to make justified and fluent analysis and to present concrete development measures. Student is well prepared to apply his/her knowledge in study and work situations. Student is able to analyze expertise in the information and communication technology sector and the development of his/her own expertise.

Assessment criteria, approved/failed

Student has achieved the course objectives. Student will be able to identify, define and use the course subject area’s concepts and models. Student understands the criteria and principles of the expertise development.

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