Deep learning is getting more important in modern medicine including radiology, pathology, surgery, neuroscience, etc. In case of radiology, there are several shortcomings in case of typical diagnostic radiology, due to the qualitative reading of a human observer. In addition, the rapid development of recent medical imaging equipment which produce a tremendous amount of image data makes the typical medical image reading nearly impractical. Recently, deep learning shows better accuracy for detection and classification in computer vision, which could be rapidly applied to medical imaging areas. I'll introduce methodology of data science including machine learning, and deep learning, and deep learning based applications in computer vision, computer aided diagnosis in medicine. In addition, I'll suggest some practical considerations on application of these technology to clinical workflow including efficient labeling technology, interpretability and visualization (No blackbox), uncertainty (Data level, Decision level), reproducibility of deep learning, novelty in supervised learning, one-shot or multi-shot learning due to Imbalanced data set or rare disease, deep survival, and physics induced machine learning.
1. Introduction to data science, machine learning, and deep learning
2. Deep learning in computer vision and applicaions
3. Deep learning for computer aided detection/diagnosis in radiology
4. Deep learning for computer aided detection/diagnosis in pathology
5. Practical consideration for deep learning application in medicine- efficient labeling technology
- Interpretability and visualization (No blackbox)
- Uncertainty (Data level, Decision level)
- Reproducibility of deep learning
- Novelty in supervised learning
- One-shot or multi-shot learning due to Imbalanced data set or rare disease
- Deep survival
- Physics induced machine learning