Preliminary Programme
 
27th May
 
Session I - Introduction to image analysis
 
14h-17h
Digital Pathological Images - Pedro Faísca
What is an image? - Anna Pezzarossa
Coffee Break
Pyramidal and large Images, RGB, colormaps, stain vectors - Gaby Martins
Concepts of threshold, ML and DL in image segmentation - Mariana Ferreira
 
28th May
 
Session II - Introduction to QuPath
 
9h30-10h15
Basic Concepts
Image properties
Create a project
Questions
 
10h15-11h15
Tools - Annotations
Types of ROIs available and how to use them
Hierarchy
Properties and classes
Calculating features
Exercises and Questions
 
11h30-12h30
Tools - Detections
Cell detection
Properties, Measurements and Tips
Positive cell detection
Exercises and Questions

Session III - Brightfield Images
 
14h-14h45
Stain Vectors
Setting a stain vector
Estimating a stain vector
Questions
14h45-15h45
Pixel Classification
Tissue detection
Create and measure objects
Exercises and Questions
 
15h45 - 16h   
Coffee Break
 
16h-16h30      
Object Classification 
Training an object classifier (machine learning)
Questions
 
16h30 - 17h30
Density Maps
Creating a density map
Finding hotspots
Creating annotations based on density
Questions
 
29th May
 
Session IV - Fluorescence Images
 
9h30-10h30
Multiplexed analysis
Visualization of multiple channels
Cell detection
Creating a cell classifier
Heatmaps
Exercises and Questions
 
10h30-11h30
Object Classification
Training an object classifier (machine learning)
Training Images
Composite Classifiers
Improving training 
Questions
 
11h30-11h45
Coffee Break
 
11h45-12h15
Pixel Classification
Training a pixel classifier (machine learning)
Creating objects based on classifier
Questions
 
Session V - Automated workflows
 
12h15-13h
Introduction to groovy scripting
Automated scripts
Stardist Extension (deep learning)
Questions
Wrap-up