Deep Learning and Computer Vision in Health (6 op)
Toteutuksen tunnus: SY00CB00-3002
Toteutuksen perustiedot
- Ajoitus
-
01.08.2026 - 31.12.2026
Toteutus ei ole vielä alkanut.
- Opintopistemäärä
- 6 op
- Lähiosuus
- 0 op
- Virtuaaliosuus
- 6 op
- Toteutustapa
- Etäopetus
- Yksikkö
- 30 Ylemmät ammattikorkeakoulututkinnot
- Toimipiste
- Laurea Verkkokampus
- Opetuskielet
- englanti
- Paikat
- 20 - 30
- Koulutus
- Degree Programme in Managing Digital Transformation in the Health Sector (NYD1), Laurea Virtual Campus
- Degree Programme in Managing Digital Transformation in the Health Sector (SYD1), Laurea Virtual Campus
Tavoitteet
The student is able to
- Identify an image's representation across various color spaces within the frequency domain and execute common image processing operations.
- Extract and analyze fundamental features from an image, design a convolutional neural network (CNN) architecture, and create an automated learning system employing traditional algorithms for image content classification.
- Develop comprehension of the standard architecture of a convolutional neural network (CNN) and its operational principles.
- Solve a moderately intricate image classification problem involving deep learning algorithms for the identification of image objects. Utilize transfer learning or fine-tuning techniques based on pre-trained CNNs.
- Apply deep learning algorithms to identify image objects and autonomously generate multimedia content in healthcare applications. Implement these algorithms using the open CV library and TensorFlow.
Arviointiasteikko
H-5