Deep Learning-Based Classification and Explainability of Cytomegalovirus Encephalitis in Longitudinal MRI Data

Type: Project

Status: running

Supervisors: Annette Schwarz, Linda-Sophie Schneider

This Master Project focuses on developing and evaluating advanced deep learning methodologies for the automated detection and classification of Cytomegalovirus (CMV)-induced encephalitis using clinical Magnetic Resonance Imaging (MRI) data.

Motivation and Goal

CMV encephalitis is a challenging condition to diagnose, and advanced, non-invasive computational methods are required to assist clinical decision-making. The primary goal is to leverage the temporal and multi-modal information within longitudinal MR scans to classify the presence or stage of inflammation (encephalitis).

Data and Scope

The project utilizes a unique, high-quality, pre-selected longitudinal dataset comprising MRI scans from approximately 300 patients, with an average of six scan time points and multiple MR sequences available per visit.

Key Tasks and Research Questions

  1. Literature Review: Conduct a targeted literature review of similar projects focused on MR classification, specifically those dealing with longitudinal data (e.g., the BraTS Challenge: Predicting the Tumor Response During Therapy) and methods for medical image classification and prediction.
  2. Model Development: Adapt and implement state-of-the-art deep learning architectures (e.g., 3D Convolutional Neural Networks, Recurrent Neural Networks, or hybrid models) suitable for processing longitudinal and multi-sequence volumetric data.
  3. Explainability (XAI): A critical component of the project is the integration of Explainable AI techniques (e.g., Grad-CAM, Saliency Mapping, or LRP). The student will implement and evaluate these methods to highlight which anatomical regions or temporal patterns contribute most significantly to the model’s classification decision, thereby increasing clinical trust and interpretability.
  4. Evaluation: The core research question is whether robust classification performance can be achieved on the available data, considering potential constraints such as fewer advanced sequences or less acute disease stages compared to published reference literature. Performance will be measured using metrics like AUC, sensitivity, and specificity.