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AI Biomedical Applications Workshop

IntermediateCourse

In three fascinating projects, learn how to create biomedical AI applications and deploy them. First, you'll discover the basics of AI and machine learning using Python and Scikit-Learn, building a model to detect Parkinson's disease from voice patterns. Next, you'll dive into deploying a Parkinson's detection app using Docker and Kubernetes, no prior knowledge is needed. Finally, using PyTorch and computer vision techniques, you'll develop an algorithm that identifies metastatic cancer from digital pathology scans. By the end, you'll have the skills to tackle real-world biomedical problems.

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Healthcare

Skills You Will Learn

  • Artificial Intelligence, Machine Learning, Data Visualization, Data Analysis

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 3 hours

Platform

  • SkillsNetwork

Last Update

  • April 12, 2023
About this Course
This course covers machine learning in the biomedical field through three projects. You will learn how to build and deploy AI models, including detecting Parkinson's Disease and identifying metastatic cancer. By the end, you'll be equipped with practical skills to apply machine learning to real-world problems and contribute to healthcare innovation. Part 1. parkinson detection; Part 2. deploy the docker container of the parkinson app; Part 3. cancer detection with pytorch.

Course Syllabus

Part 1: Using Machine Learning to Analyze Voice Disorders for Parkinson's Disease Detection
  • Introduction to machine learning and its applications in Biomedicine
  • Understanding voice disorders and Parkinson's disease
  • Implementing different machine learning algorithms such as decision trees and support vector machines
  • Conducting grid search to optimize model parameters
  • Visualizing the models for interpretation and feature identification
  • Building a machine learning model that can accurately predict Parkinson's disease based on voice recordings
Part 2: Deploying AI Application on IBM Code Engine
  • Introduction to IBM Code Engine and its features
  • Understanding serverless platforms and their advantages
  • A step-by-step guide to deploying the AI application on IBM Cloud using IBM Code Engine
  • Using Parkinson's detection model as an example
  • Creating a Docker container image with Kubernetes for app deployment
Part 3: Cancer Detection with PyTorch
  • Introduction to the convolutional neural network and transfer learning
  • Understanding pre-trained CNNs
  • Dataset preparation for PCAM images
  • Training and testing the model
  • Improving model performance using transfer learning
Overall, this course will provide an in-depth understanding of machine learning applications in biomedicine, from detecting Parkinson's disease to identifying metastatic cancer. By the end of the course, students will have the skills to build and deploy AI models and contribute to healthcare innovation.

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Sina Nazeri

Data Scientist at IBM

I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.

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