From Supervised to Self-Supervised: A JEPA Encoder for Coronary Artery Calcification Detection in 2D Angiography

Forschungsthema/Bereich

Medizinische Bildgebung für Modellierung und Simulation

Typ der Abschlussarbeit

Master

Startzeitpunkt

-

Bewerbungsschluss

30.04.2027

Dauer der Arbeit

-

Beschreibung

Motivation
Coronary artery calcification (CAC) is a key determinant of the complexity and risk of percutaneous coronary interventions. However, in the catheterisation laboratory, CAC is still primarily assessed through visual inspec- tion of 2D coronary angiography (2DCA) images, which can be subjective and challenging.
Our group recently developed a deep learning workflow for automatic CAC detection based on a double-head ResNet-18 encoder and a U-Net-like decoder *. However, the encoder is trained from scratch on a relatively small annotated cohort, which may limit the robustness and generalisability of the resulting model.
Joint Embedding Predictive Architectures (JEPA) offer an alternative approach by learning meaningful visual representations from large collections of unlabelled images, without relying on negative sampling. This makes them particularly promising for medical imaging, where large annotated datasets are often difficult to obtain. In this thesis, a JEPA-based vision encoder will be pretrained on large public coronary angiography datasets and subsequently integrated into the existing CAC detection workflow, replacing its supervised backbone.

Student Project
The project aims to assess whether self-supervised pretraining on public angiography data improves CAC identification over the current supervised baseline.

• Collection and harmonisation of public 2DCA datasets (ARCADE, CADICA, AngioCAD) • Self-supervised pretraining of a ViT encoder with a LeJEPA / SIGReg objective
• Integration of the pretrained encoder into the existing double-head workflow
• Benchmarking against the published baseline (F1-score, IoM, Hausdorff distance)

*: De Rose & Raggio et al. “A multi-model deep learning approach for the identification of coronary artery calcifications within 2D coronary angiography images”. In: International Journal of Computer Assisted Radiology and Surgery 20 (2025), pp. 1273–1281.

Voraussetzung

Voraussetzungen an Studierende

• General knowledge in Computer Science and Biomedical Engineering.

Studiengangsbereiche

• Ingenieurwissenschaften
  Biomedical Engineering
  Electrical Engineering and Information Technology
  Medizintechnik
• Naturwissenschaften und Technik
  Biologie
• Sonstiges z.B. Lehramt
  Informatik

Betreuung

Titel, Vorname, Name

M.Sc. Ciro Benito Raggio, Dr.-Ing. Edoardo De Rose

Organisationseinheit

Institut für Biomedizinische Technik

E-Mail Adresse

ciro.raggio@kit.edu

Link zur eigenen Homepage/Personenseite

Website

Bewerbung per E-Mail

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E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ciro.raggio@kit.edu


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