AI for Healthcare

AI for Healthcare Nanodegree Program Introduction

Introduction to AI for 2D Medical Imaging

In this lesson, you will be given an introduction to this course about AI for 2D medical imaging, why AI is important and where AI fits in the space.

Clinical Foundations of 2D Medical Imaging

In this lesson, we will cover clinical foundations such as clinical workflows, applications of 2D imaging in clinical settings and how machine learning impacts clinics.

2D Medical Imaging Exploratory Data Analysis

In this lesson, we will learn the DICOM standard in medical imaging, and how to explore medical imaging data and prepare it for machine learning applications.

Classification Models of 2D Medical Images

In this lesson, we'll dive deep into classification tasks for 2D medical imaging using different machine learning models. and we will talk about pre-process data, train, test, and validate models.

Translating AI Algorithms for Clinical Settings with the FDA

In this lesson, you will learn about how your work fits into the bigger picture, and how it’s regulated by the FDA, which is an often-overlooked, but incredibly important.

Project: Pneumonia Detection from Chest X-Rays

Introduction to AI for 3D Medical Imaging

In this lesson, we will introduce the course and instructors. We will give you an overview of the context for AI in 3D medical imaging space, and cover the objectives of the course.

3D Medical Imaging - Clinical Fundamentals

In this lesson, we cover the basic terminology and concepts related to 3D medical imaging. We will look at the problem space from a clinical standpoint and learn how CT and MR scanners produce images.

3D Imaging Exploratory Data Analysis

In this lesson, we will dive deeper into medical imaging formats NIFTI and DICOM, how scanner data is represented, and how to read medical volumes stored in these files and analyze them.

3D Medical Imaging - End-to-End Deep Learning Applications

In this lesson, we cover the basics of building deep neural networks for 3D medical imaging (mostly segmentation & classification) and performance evaluation from a software and clinical perspective.

Deploying AI Algorithms in Real World Scenarios

In this lesson, we'll talk about clinical networks, architecture, and AI deployment, tools and their use by data scientists and clinicians, as well as medical device regulation and data privacy.

Hippocampal Volume Quantification in Alzheimer's Progression

In this project, you will curate a dataset of brain MRIs, train a segmentation on a CNN, and integrate this into a clinical network to quantify hippocampal volume for Alzheimer's progression.

Applying AI to EHR Data Introduction

This lesson will provide you with an Introduction to the EHR Data course outline, content, as well as introduce you to your instructor.

EHR Data Security and Analysis

In this lesson, you will learn about the importance of data security and the different standards that apply to EHR, as well as analyzing EHR data.

EHR Code Sets

In this lesson you will learn how to work with different EHR codes and how to map them properly to records.

EHR Transformations & Feature Engineering

In this lesson, you'll gain skills in feature engineering and transformation of EHR.

Building, Evaluating and Interpreting Models

In this final lesson, you'll be putting all of your skills together to build, evaluate and interpret ML models for Bias and Uncertainty.

Project: Patient Selection for Diabetes Drug Testing

In this project students will use what they learn in the classroom to apply AI in healthcare for patient data.

Introduction to Wearable Data

We’ll cover what wearables are and the scope of the class. Learn who your instructor is and his thoughts on the promise and caveats of wearables in medical research and decision making.

Intro to Digital Sampling & Signal Processing

A brief tour through sampling theory, signal processing, the Fourier transform, and other related topics. We’ll briefly cover some plotting and visualization techniques here as well.

Introduction to Sensors

We cover the basics of the accelerometer, the PPG sensor, and the ECG sensor, as well as what these signals look like in typical environments and the types of noise that we will encounter.

Activity Classification

Build an activity classifier using a wrist-worn accelerometer!

ECG Signal Processing

We deep dive into a fundamental algorithm for ECG processing and use that as the basis for an arrhythmia detection algorithm.

Motion Compensated Pulse Rate Estimation

In this project, you will create a pulse rate algorithm that takes into account activity and apply this algorithm to a new data set to determine clinically significant features.

Take 30 Min to Improve your LinkedIn

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Optimize Your GitHub Profile

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.