Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Stanford University

Few-Shot Chest X-Ray Diagnosis Using Clinical and Literature Images

Stanford University via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore few-shot learning techniques for chest X-ray diagnosis in this Stanford University seminar presented by Dr. Angshuman Paul. Delve into two novel methods: a discriminative ensemble trained on clinical images and a model utilizing both scientific literature and unlabeled clinical chest X-rays. Compare these approaches to existing few-shot learning methods and understand their superior performance. Gain insights into the challenges of few-shot learning in radiology image analysis and learn about ensemble models, bootstrap sampling, subspace sampling, and meta-learning frameworks. Examine the experimental results, including F1 scores and utility of design, and consider future directions in this field. Engage with the speaker's expertise in machine learning, medical imaging, and computer vision during the interactive Q&A session following the presentation.

Syllabus

Introduction
Outline
Fewshot Learning
Fewshot Challenges
Ensemble Models
Two Methods for Chest Xray Diagnosis
Ensemble Learning
Bootstrap sampling
Projecting bootstrap samples
Subspace sampling
Winner subspace
Subspace dimension
Clusterbased representation
Hidden space representation
Weighted voting
Query input
Process Pipeline
Auto Encoder Ensemble
Class Levels
Metalearning Framework
Experiments
Combinations
Training Data
Results
F1 Scores
Utility of Design
Conclusion
Questions
Future Sector
Classification Pipeline
Initial Training
Loss Function
Pseudo Levels
Retraining
Performance
Result

Taught by

Stanford MedAI

Reviews

Start your review of Few-Shot Chest X-Ray Diagnosis Using Clinical and Literature Images

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.