This course on graph-based modeling in computational pathology aims to teach participants about leveraging cellular interactions and spatial structures in whole slide images using graph neural networks. The learning outcomes include understanding graph-based modeling methods, such as cell graphs, graph neural networks, and graph clustering, in the context of computational pathology. The course covers skills like constructing cell graphs, node embedding, and cluster interpretation. The teaching method involves a journal club format led by a PhD candidate, including interactive discussions and Q&A sessions. The intended audience includes individuals interested in AI applications in medicine, particularly in computational pathology and medical machine learning.
Overview
Syllabus
Introduction
Outline
Spectral networks
Spatial networks
Computational pathology
Cell graphs
Cell graph convolutional network
Method overview
Cell graph construction
What is graph adaptive glossage
What is adaptive glossage
Node embedding
Graph clustering
Graph class
Concatenation
Experiments
Cluster assignments
Cluster interpretation
Cancer grading
Nuclear sampling
Overview
Graph construction
Graph new network
Quick question
Paper
Drawbacks
Posthoc graph expanders
Histograms
Separability Score
Aggregate
Final Score
Risk Score
Data Set
Qualitative Assessment
Quantitative Assessment
Personal takeaways
Domain expertise
Explanation explainers
Graph neighborhood sampling
Questions
Taught by
Stanford MedAI
Tags
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Reviews
4.5 rating, based on 2 Class Central reviews
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his course on graph-based modeling in computational pathology aims to teach participants about leveraging cellular interactions and spatial structures in whole slide images using graph neural networks. The learning outcomes include understanding graph-based modeling methods, such as cell graphs, graph neural networks, and graph clustering, in the context of computational pathology. The course covers skills like constructing cell graphs, node embedding, and cluster interpretation.
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The Course was very helpful and informative. I have learned a lot of things in detail. The graphs and also new synynoms using within the subject course. The course in general was very useful. It helped me gain practical knowledge through extensive practice and research. It helped with any queries and guided us step by step through the process.
