Welcome to the AI for Medicine Specialization!!

This repository is a summary of "AI for Medicine Specialization" lecture from Deeplearning.ai

The summary here includes

• Lecture Notes
• Example Codes
• Assignments

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These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.

There are 3 Courses for "AI for Medicine Specialization" which are:

• The nuances of working with 2D and 3D medical data
• Convolutional Neural Network for diagnosis of lung disease by looking at a chest X-ray.
• Segmentation model for detecting brain disorders.

Week 1: You build a deep learning model that can interpret chest X-rays to classify different disease causes.
Week 2: You'll implement evaluation methodologies to assess the quality of your model.
Week 3: You use image segmentation to identify the location and boundaries of brain tumors in MRI scans.

• Learn how to work with Structured data
• Risk models and survival estimators for heart disease using tree-based statistical models to improve patient survival estimates.
• Random forest predictor to determine patient prognosis

• Build a treatment effect predictor, apply model interpretation techniques
• Natural language processing to extract information from radiology reports
• Explore how Natural language extraction can more efficiently label medical datasets.