This course aims to teach learners about the application of deep learning in medical diagnoses. The learning outcomes include understanding traditional clinical diagnostic procedures, utilizing machine learning frameworks, detecting abnormalities in medical images such as X-rays, interpreting diagnostic results, and exploring the future of diagnostic support with artificial intelligence. The course covers topics such as arrhythmia detection, continuous monitoring, network architectures like Residual Networks and Wide ResNets, and the challenges of automated detection in the medical field. The teaching method involves lectures on various topics related to deep learning in medical diagnoses. This course is intended for healthcare professionals, data scientists, researchers, and anyone interested in the intersection of artificial intelligence and healthcare.
Overview
Syllabus
Introduction.
Traditional Model of Clinical.
Diagnostic Procedure with Al.
Information Gathering Step.
Diagnostic Testing with Al.
Machine Learning Framework.
Next Paradigm shift?.
Arrhythmia detection.
Future of continuous monitoring.
Holter Monitor.
Amount of data capture.
Automated Detection Challenges.
Previous Approaches.
Setup.
Network Architecture.
Residual Networks.
Wide ResNets.
Dataset - Test Set.
Medical Errors.
Chest Radiograph Interpretation.
Chest X-ray exam.
Detecting Abnormalities.
X-ray findings of pneumonia.
Detecting Pneumonia.
DenseNets.
Dataset - Train Set.
Evaluation -- Limitations.
Class Activation Maps.
Interpretations.
Future of diagnostic access.
Diagnosis Clinical Decision Support.
Knee MR.
Input.
Dataset Training on.
Interpretability.
External Validation.
Diagnostic Future with Al.
Taught by
Stanford Online
