This is main repository to group the my course work as part of Deeplearning.ai - AI for Medicine Specialization by deeplearning.ai.
- Pranav Rajpurkar, Instructor, PhD Candidate Stanford University
- Bora Uyumazturk
- Amirhossein Kiani
- Eddy Shyu
- Course 1: AI for Medical Diagnosis
- Course 2: AI for Medical Prognosis
- Course 3: AI for Medical Treatment
- Although radiology image classification and segmentation using a CNN in TensorFlow is interesting, survival analysis using different traditional ML methods are a nice comparison and contrast between modern and classical AI, ML and DL approaches.
- GradCAM is a novel approach to overlay heatmaps on radiology images.
- In Healthcare, there are common data challenges of: data access, data size, and data quality.
- Validation need clear methods, evaluation metrics and explanation.
- Despite the hype of AI and deep learning (DL), traditionl ML methods (e.g, decision trees, random forest) still very effective in real world scenarios.