Course detail

Artificial Intelligence in Medicine

FEKT-BPC-UIMAcad. year: 2023/2024

The course focuses on the basic types of neural networks (with backpropagation, Hamming and Kohonen network). The second part focuses on the hierarchical and non-hierarchical cluster analysis. The third part is focused on the theory of fuzzy sets, fuzzy relations, fuzzy logic, fuzzy inference and approximate reasoning procedures. The following are the methods for relevant features selection and for evaluation of the results obtained by above tools of artificial intelligence.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Marina Filipenská, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Entry knowledge

The knowledge on the Bachelor´s degree level is requested, namely on numerical mathematics. The laboratory work is expected knowledge of Matlab programming environment.

Rules for evaluation and completion of the course



Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
Basically:
- obligatory computer-lab tutorial (missed labs must be properly excused and can be replaced after agreement with the teacher)
- voluntary lecture.

Aims

Gaining knowledge about artificial neural networks, introduction to Hierarchical and non-hierarchical cluster analysis. Introduction to the theory of fuzzy sets, fuzzy relations, fuzzy logic, fuzzy inference methods as approximate reasoning. Furthermore, methods for relevant features selection and for evaluation of the results obtained using above approaches.
The graduate of the course is capable of:
- understanding principles of neural networks (with backpropagation errors, Hamming, Kohonen network),
- implementing the cluster analysis using the non-hierarchical or hierarchical methods,
- explaining the principle of fuzzy inference and approximate reasoning,
- performing selection of the most relevant features for further analysis,
- evaluating the performance of machine learning algorithms,
- giving examples of biomedical areas where above methods are widely used.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Kozumplík, J., Provazník, I.: Umělá inteligence v medicíně. Elektronická skripta. ÚBMI FEKT VUT v Brně, Brno, 2007. (CS)

Recommended reading

Šnorek, M.: Neuronové sítě a neuropočítače. Skripta ČVUT, Praha, 2002 (CS)

eLearning

eLearning: currently opened course

Classification of course in study plans

  • Programme BPC-BTB Bachelor's, 3. year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Ing. Marina Filipenská, Ph.D.

Syllabus

1. Introduction to Artificial Intelligence.
2. Artificial neural networks, the neuron and its characteristics, the neuron as a classifier.
3. Learning the neuron with binary and real inputs and outputs, single-layer perceptron.
4. Multi-layer feed-forward network, the backpropagation algorithm.
5. Hamming network, Kohonen network.
6. Cluster analysis, hierarchical cluster analysis.
7. Non-hierarchical cluster analysis, k-means algorithm.
8. Fuzzy sets, fuzzy relations.
9. Logic, fuzzy logic, fuzzy inference, approximate reasoning.
10. Feature methods for classification.
11. The logic of decision-making systems, predicate logic.
12. Inference and substantiation of claims.

Exercise in computer lab

26 hours, compulsory

Teacher / Lecturer

MUDr.Ing. Richard Ředina

Syllabus

1. One artificial neuron without learning.
2. Using the neuron as a classifier.
3. Learning the neuron, δ-rule. Perceptron in Neural Network Toolbox.
4. The neural network without learning.
5. Neural network, the backpropagation (BP) algorithm.
6. BP feed-forward network in Neural Network Toolbox (example classification ECG cycles).
7. BP feed-forward network in Neural Network Toolbox (example approximation signals).
8. Hierarchical cluster analysis.
9. Non-hierarchical cluster analysis.
10. PCA decorrelation and symptoms.
11. Fuzzy cluster analysis.
12. Fuzzy inference and approximate reasoning.

eLearning

eLearning: currently opened course