This course focuses on developing interpretable diagnostic and prognostic machine learning models for digitized histopathology slides without manual annotation. The learning outcomes include understanding a general framework for creating these models, scaling them to large datasets, and applying them to tasks like cancer subtyping and predicting tumor origins. The course teaches skills such as segmentation, embedding, attention pooling, and using benchmarks and attention scores. The teaching method involves a talk followed by interactive discussions and Q&A sessions. The intended audience includes individuals interested in AI, medicine, computational pathology, and spatial biology.
Weakly-Supervised, Large-Scale Computational Pathology for Diagnosis and Prognosis - Max Lu
Stanford University via YouTube
Overview
Syllabus
Introduction
Welcome
Background
Example
General workflow
Can we train accurate diagnostic or problem prognostic models
The same label assumption
Multiple instance learning
Data efficiency
Recap
Framework
Segmentation
Embedding
Attention pooling
Summary
Benchmarks
Attention scores
Cell phone microscopy
Results
Summarize
Code
Prognosis
Primary origins of ceps
Study design
Classification
Heatmaps
Interactive demo
Attention heating map
Dummy tool
High certainty diagnosis
Differential diagnosis
Thank you
Which regions in the slide will contribute
Can the primary originate from one single primary
Is the morphology more nuanced
Clustering
Outro
Taught by
Stanford MedAI
