AI Courses

Lecture 1: Basic of AI: What are DNN and how they work?
Lecture 2: The design of an AI project: data, annotations, validation, error metrics
Lecture 3: Case Study – AI for Pathology from A to Z
Lecture 4: Take your annotations to the maximum ! How AI can help you create high quality and huge annotated data sets.
Lecture 5: Supervised, Weakly Supervised, Selfsupervised – what do they mean and what are they useful for?
Lecture 6: XAI – can we understand how AI makes decisions?

Session 1:Introduction to AI and the Basics of Deep Neural Networks
Session 2:How to train a Deep Neural Network
Session 3:Basic Deep Learning Architectures
Session 4: Metrics to evaluate the performance of AI
Session 5: Advanced AI Architectures
Session 6: AI in Pathology

• Session 1: Introduction to AI and DL architectures
• Session 2: Neural networks: architecture, training & optimization
• Session 3: Deep Learning Architectures: Classification
• Session 4: Segmentation, Detection, GANS, Weakly Supervised, Transfer Learning
• Session 5: XAI & Accuracy Metrics
• Session 6: AI for Pathology in practice with the DeePathology STUDIO

• Session 1: Introduction to Image Analysis and Deep Learning
• Session 2-4: Neural Networks Training & Architectures
• Session 5: All about DATA
• Session 6: Real life examples of Deep Learning in Pathology
• Session 7: Explainability, Validation and more
• Session 8: Wet Lab with the DeePathology™ STUDIO