🟡 Intermediate Stage: Application & Model Diversity

🎯 Objective

To develop an applied understanding of more complex architectures and scenarios, including temporal data, pathology-specific tasks, and image modalities beyond 2D.

🧠 Topics

Topic	Description
Self-Supervised Learning	Use unlabeled data with contrastive learning or predictive masking to pre-train models.
Graph Neural Networks (GNNs)	Apply GNNs to model anatomical relationships or patient similarity networks.
Multi-Modal Learning (Advanced)	Effectively fuse CT, MRI, PET, and structured clinical data for richer inference.
Federated Learning (Practical)	Explore challenges of data heterogeneity and model aggregation in clinical federations.
3D Image Processing	Extend CNNs and Transformers to volumetric data like CT and MRI scans.
AI for Ultrasound Analysis	Handle real-time, low-signal-quality imaging scenarios with AI.
Radiomics & Feature Engineering	Extract and interpret radiomic signatures for prediction and prognosis.
Anomaly Detection & OOD Generalization	Identify unexpected cases or detect failure modes in deployed AI systems.
AI for Pathology & Histopathology	Analyze gigapixel pathology slides for cancer grading and biomarker analysis.
Survival Analysis & Risk Prediction	Model time-to-event outcomes using classical and deep survival models.
Time-Series Analysis for Patient Monitoring	Analyze sequential patient data (e.g., vitals, labs) to predict deterioration.
Bayesian Deep Learning	Incorporate model uncertainty for robust medical decision-making.