AI for Healthcare
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AI for Healthcare

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Nanodegree key: nd320
Version: 4.0.0
Locale: en-us
The course is focused on using AI technologies for practical applications in health care.

Content

Part 01 : Welcome to the AI for Healthcare Nanodegree Program

Module 01: AI for Healthcare Nanodegree Program Orientation
- Lesson 01: AI for Healthcare Nanodegree Program Introduction
  - Concept 01: Welcome to Udacity
  - Concept 02: Welcome to the Nanodegree Program Experience
  - Concept 03: How to Succeed
    Part 02 : Applying AI to 2D Medical Imaging Data
Module 01: 2D Imaging
- Lesson 01: Introduction to AI for 2D Medical ImagingIn 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.
- Lesson 02: Clinical Foundations of 2D Medical ImagingIn this lesson, we will cover clinical foundations such as clinical workflows, applications of 2D imaging in clinical settings and how machine learning impacts clinics.
- Lesson 03: 2D Medical Imaging Exploratory Data AnalysisIn 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.
- Lesson 04: Classification Models of 2D Medical ImagesIn 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.
- Lesson 05: Translating AI Algorithms for Clinical Settings with the FDAIn 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.
- Lesson 06: Project: Pneumonia Detection from Chest X-RaysProject Description - Pneumonia Detection From Chest X-RaysProject Rubric - Pneumonia Detection From Chest X-Rays
  - Concept 01: Project Overview
  - Concept 02: Starting the project
  - Concept 03: Submitting the project
  - Concept 04: Jupyter GPU Workspace
    Part 03 : Applying AI to 3D Medical Imaging Data
Module 01: 3D Imaging
- Lesson 01: 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.
- Lesson 02: 3D Medical Imaging - Clinical FundamentalsIn 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.
- Lesson 03: 3D Imaging Exploratory Data AnalysisIn 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.
- Lesson 04: 3D Medical Imaging - End-to-End Deep Learning ApplicationsIn 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.
- Lesson 05: Deploying AI Algorithms in Real World ScenariosIn 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.
- Lesson 06: Hippocampal Volume Quantification in Alzheimer’s ProgressionIn 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. Project Description - Hippocampal Volume Quantification in Alzheimer’s ProgressionProject Rubric - Hippocampal Volume Quantification in Alzheimer’s Progression
Module 01: Applying AI to EHR Data
- Lesson 01: Applying AI to EHR Data IntroductionIntroduction to the EHR Data course and the instructor.
- Lesson 02: EHR Data Security and AnalysisLearn about the importance of data security and the different standards that apply to EHR. Also, learn about analyzing EHR data.
- Lesson 03: EHR Code SetsIn this lesson you will learn how to work with different EHR codes and how to map them properly to records.
- Lesson 04: EHR Transformations & Feature Engineering In this lesson, you’ll gain skills in feature engineering and transformation of EHR.
- Lesson 05: Building, Evaluating and Interpreting ModelsIn this final lesson, you’ll be putting all of your skills together to build, evaluate and interpret ML models for Bias and Uncertainty.
- Lesson 06: Project: Patient Selection for Diabetes Drug TestingIn this project students will use what they learn in the classroom to apply AI in healthcare for patient data.Project Description - Patient Selection for Diabetes Drug TestingProject Rubric - Patient Selection for Diabetes Drug Testing
  - Concept 01: Patient Selection for Diabetes Drug Testing Project
  - Concept 02: Project Instructions
  - Concept 03: Patient Selection for Diabetes Drug Testing Workspace
    Part 05 : Applying AI to Wearable Device Data
Module 01: Wearable Data
- Lesson 01: Introduction to Wearable DataWe’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.
- Lesson 02: Intro to Digital Sampling & Signal ProcessingA 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.
- Lesson 03: Introduction to SensorsWe 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.
- Lesson 04: Activity ClassificationBuild an activity classifier using a wrist-worn accelerometer!
- Lesson 05: ECG Signal ProcessingWe deep dive into a fundamental algorithm for ECG processing and use that as the basis for an arrhythmia detection algorithm.
- Lesson 06: Motion Compensated Pulse Rate EstimationIn 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. Project Description - Motion Compensated Pulse Rate EstimationProject Rubric - Motion Compensated Pulse Rate Estimation
Module 02: Career Services
- Lesson 01: Take 30 Min to Improve your LinkedInFind your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.Project Description - Improve Your LinkedIn ProfileProject Rubric - Improve Your LinkedIn Profile

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人工智能系列-2022

AI智能医疗 4.0/AI for Healthcare

AI for Healthcare

Content

Part 01 : Welcome to the AI for Healthcare Nanodegree Program

Part 02 : Applying AI to 2D Medical Imaging Data

Part 03 : Applying AI to 3D Medical Imaging Data

Part 04 : Applying AI to EHR Data

Part 05 : Applying AI to Wearable Device Data