Checking date: 28/04/2023

Course: 2023/2024

Artificial Intelligence in radiology and microscopy
Master in Machine Learning for Health (Plan: 480 - Estudio: 359)

Coordinating teacher: MUÑOZ BARRUTIA, MARIA ARRATE

Department assigned to the subject: Bioengineering Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
It is recommended to have passed or to have previous background in the subjects of Biomedical Image Processing, Machine Learning, and Deep Learning.
The 'Artificial Intelligence in Radiology and Microscopy' course explores the intersection of AI, radiology, and microscopy, teaching students to apply machine learning and computer vision techniques to medical image analysis. Covering topics such as segmentation, classification, and automated diagnosis, students will engage in hands-on exercises and interdisciplinary collaboration to gain practical skills and address the ethical implications of AI-driven medical imaging. The particular objectives of the course are: - To provide a comprehensive understanding of the principles, techniques, and applications of AI in the fields of radiology and microscopy. - To familiarize students with the latest advancements in AI technologies, such as deep learning, machine learning, and computer vision, and their role in improving the accuracy and efficiency of radiological and microscopy image analysis. - To develop proficiency in using AI tools and algorithms for the identification, segmentation, and classification of medical images, including X-rays, CT scans, MRI scans, and microscopic slides. - To equip students with the skills required to critically evaluate the strengths, limitations, and ethical implications of AI applications in radiology and microscopy. - To encourage interdisciplinary collaboration between computer scientists, engineers, radiologists, and pathologists, fostering a deeper understanding of the potential synergies between these fields. - To promote a culture of innovation and research in the application of AI to radiology and microscopy, inspiring students to contribute to the development of new algorithms, techniques, and solutions that address existing challenges and emerging needs in these fields. - To prepare students for careers in AI-driven medical imaging, providing them with the knowledge and skills needed to excel in research, industry, and clinical settings.
Skills and learning outcomes
Description of contents: programme
1. AI in Biomedical Imaging - Historical background - Recent developments - Impact of methods on research and clinical practice 2. Evaluation of AI-based methods - Public databases - Challenges 3. AI-based methods in microscopy - Noise reduction and preprocessing - Segmentation - Object detection and tracking - Practical considerations and existing solutions 4. AI-based methods in radiology and radiotherapy - Segmentation - Classification and automatic diagnosis - Treatment response prediction - Practical considerations and existing solutions 5. Ethical considerations and data protection
Learning activities and methodology
AF3 Theoretical practical classes AF4 Laboratory practices AF6 Team work AF7 Student individual work AF8 Partial and final exams Activity code total hours number presencial hours number non-presencial hours number AF3 84 84 0 AF4 63 63 0 AF6 90 0 90 AF7 222 0 222 AF8 9 9 0 TOTAL MATERIA 468 156 312
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Chen, P. M.,. Deep learning: an update for radiologists. Radiographics, 41(5), 1427-1445. 2021
  • Ronneberger, O., Fischer, P., Brox, T.. U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention ¿ MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham. 2015
  • Volpe, G.. Roadmap on deep learning for microscopy. ArXiv. 2023
  • Zhou, S., Greenspan, S.K., Shen, D.. Deep learning for medical image analysis. Academic Press. 2017
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Chan, H.P., Samala, R.K., Hadjiiski, L. J., Zhou, C.. Deep learning in medical image analysis. Adv Exp Med Biol, 1213:3-21. 2020
  • Cohen, S.. Artificial intelligence and deep learning in pathology. Elsevier Health Sciences. 2020
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.