Students

BA/MA Thesis: Deep Learning for Inherited Retinal Diseases Detection

Abstract. Our project aims to improve the diagnosis of Inherited Retinal Dystrophies (IRDs), a group of rare retinal diseases impacting over 2 million people globally [1]. IRDs can lead to vision problems like night blindness, color blindness, tunnel vision, and eventual blindness, greatly affecting patients’ and their families’ quality of life [4].

MA Thesis: Deep Learning based detection model of the temporal and axillary artery in suspected giant cell arteritis in ultrasound images

Abstract. Giant cell arteritis (GCA) is a systemic autoimmune disease marked by inflammation of blood vessels (“vasculitis”) that can cause impairment and damage to organs [1]. GCA typically affects large and medium size arteries, such as the aorta and the temporal and axillary arteries [2–4].

MA Thesis: Deep Learning based model for detection and grading of prostate cancer using mpMRI and MR-Fingerprinting

Abstract. Prostate cancer (PCa) is the most common cancer in men and the second leading cause of cancer death in Germany [4,14]. Both digital rectal examination (DRE) along with the prostate-specific antigen (PSA) level in blood samples are typically used in PCa screening.

MA Thesis: Deep Learning-based method for virtual ECV in cardiac magnetic resonance imaging

Abstract. Diseases of the cardiovascular system are among the most common diseases worldwide and are the leading cause of death. The World Health Organization (WHO) estimates that about 17.9 million people die of cardiovascular diseases each year worldwide.

Course: Introduction to Machine Learning

Machine Learning has gained a lot of momentum within development organizations that are actively looking for innovative solutions to leverage their data to identify new levels of understanding their operations and processes. Machine learning is a subfield of Artificial Intelligence where the machine learns from data rather than from explicit programming.

Seminar: Federated Learning in Healthcare (SoSe2021)

Organizers: Dr. Shadi Albarqouni, Helmholtz AI and TU Munich, Prof. Nassir Navab, Chair for Computer Aided Medical Procedures, and Prof. Daniel Rueckert, Chair for AI in Medicine, TU Munich Time: Fridays, 10:00 - 12:00

Seminar: Federated Learning in Healthcare (WiSe2020)

Organizers: Dr. Shadi Albarqouni, Helmholtz AI and TU Munich, and Prof. Nassir Navab, TU Munich. Announcements - 17-12-2020: The deadline to submit the blog post is moved to 1st. Feb. 2021.