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Amit Yadav
In this 2-hour long guided project, we will use a ResNet-18 model and train it on a COVID-19 Radiography dataset. This dataset has nearly 3000 Chest X-Ray scans which are categorized in three classes - Normal, Viral Pneumonia and COVID-19. Our objective in this project is to create an image classification model that can predict Chest X-Ray scans that belong to one of the three classes with a reasonably high accuracy. Please note that this dataset, and the model that we train in the project, can not be used to diagnose COVID-19 or Viral Pneumonia. We are only using this data for educational purpose.
Read more
In this 2-hour long guided project, we will use a ResNet-18 model and train it on a COVID-19 Radiography dataset. This dataset has nearly 3000 Chest X-Ray scans which are categorized in three classes - Normal, Viral Pneumonia and COVID-19. Our objective in this project is to create an image classification model that can predict Chest X-Ray scans that belong to one of the three classes with a reasonably high accuracy. Please note that this dataset, and the model that we train in the project, can not be used to diagnose COVID-19 or Viral Pneumonia. We are only using this data for educational purpose.
Read more
In this 2-hour long guided project, we will use a ResNet-18 model and train it on a COVID-19 Radiography dataset. This dataset has nearly 3000 Chest X-Ray scans which are categorized in three classes - Normal, Viral Pneumonia and COVID-19. Our objective in this project is to create an image classification model that can predict Chest X-Ray scans that belong to one of the three classes with a reasonably high accuracy. Please note that this dataset, and the model that we train in the project, can not be used to diagnose COVID-19 or Viral Pneumonia. We are only using this data for educational purpose.
Before you attempt this project, you should be familiar with programming in Python. You should also have a theoretical understanding of Convolutional Neural Networks, and optimization techniques such as gradient descent. This is a hands on, practical project that focuses primarily on implementation, and not on the theory behind Convolutional Neural Networks.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/oohxu4/detecting
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Syllabus
Detecting COVID-19 with Chest X-Ray using PyTorch
In this 2-hour long guided project, we will use a ResNet-18 model and train it on a COVID-19 Radiography dataset. This dataset has nearly 3000 Chest X-Ray scans which are categorized in three classes - Normal, Viral Pneumonia and COVID-19. Our objective in this project is to create an image classification model that can predict Chest X-Ray scans that belong to one of the three classes with a reasonably high accuracy. Please note that this dataset, and the model that we train in the project, can not be used to diagnose COVID-19 or Viral Pneumonia. We are only using this data for educational purpose. Before you attempt this project, you should be familiar with programming in Python. You should also have a theoretical understanding of Convolutional Neural Networks, and optimization techniques such as gradient descent. This is a hands on, practical project that focuses primarily on implementation, and not on the theory behind Convolutional Neural Networks.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in working with computer vision
Appropriate for learners who are familiar with Python, convolutional neural networks, and gradient descent
Employs a practical, hands-on approach to reinforce learning
Focuses on implementation rather than advanced theoretical concepts
Utitlizes the ResNet-18 model for image classification
Emphasizes the importance of practical, hands-on learning
Save this course
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Reviews summary
Engaging pytorch project for covid-19 detection
Learners say this course is a well-received project that applies PyTorch to the detection of COVID-19. Beginners should be aware that prior experience with PyTorch will help with understanding more conceptual elements. The course includes code that allows learners to practice using Convolutional Neural Networks. Overall, this course has great explanations, is easy to understand, and offers a practical opportunity to practice detecting disease using deep learning and machine learning techniques.
Easy to understand explanations and code
"Good explanations + code. Everything so smooth and understandable. Great lector!"
"Every step is thoroughly explained."
"Nice and concise course"
Instructor has a hard to follow accent
"The intructor knowledge is okay but his accent is extremely hard to follow, the auto script can barely catch any of his words."
"Ryhme is terrible"
Can be challenging for beginners
"Overall, the material it's very challenging for me because I haven't used PyTorch before."
"This is not at all a beginner level project, and one should only undertake it if they want to practice Convolutional Neural Networks specifically."
"If you have already some Pytorch experience, this course might add value to you but if you have some basic experience with Python (pandas, numpy, sklearn, matplotlib), this course will not take your ML skills to the next level."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Detecting COVID-19 with Chest X-Ray using PyTorch with these
activities:
Join a study group to discuss the course material
Show steps
Engage with fellow learners, exchange ideas, and clarify any doubts you may have.
Show steps
-
Find or create a study group with classmates
-
Meet regularly to discuss the course content and work on assignments together
Organize your course notes, assignments, and study materials
Show steps
Establish a structured system for managing your course materials, aiding in effective review and retention.
Show steps
-
Create a dedicated folder or digital notebook for your course materials.
-
File and name your notes, assignments, and study materials consistently.
-
Consider using different colors or tags to categorize different types of materials.
Solve practice problems on image classification
Show steps
Sharpen your problem-solving skills and reinforce your understanding of image classification concepts.
Browse courses on
Image Classification
Show steps
-
Solve coding challenges on platforms like LeetCode or HackerRank
-
Participate in Kaggle competitions related to image classification
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Review Python programming
Show steps
Brush up on your Python skills to strengthen your foundation for the course.
Browse courses on
Python Programming
Show steps
-
Review Python syntax and data structures.
-
Solve beginner-level Python coding problems.
Participate in a study group with other course participants
Show steps
Engage with peers, discuss course content, and enhance understanding through collaboration.
Show steps
-
Find or form a study group with other course participants.
-
Set regular meeting times to discuss course material and assignments.
-
Take turns presenting and explaining concepts to reinforce your understanding.
-
Collaborate on practice exercises and problem-solving.
Create a ResNet-18 model from scratch
Show steps
Build a solid understanding of ResNet-18 architecture and implementation.
Browse courses on
Convolutional Neural Networks
Show steps
-
Set up a PyTorch environment
-
Define the ResNet-18 architecture
-
Implement the forward and backward passes
Follow a tutorial on Convolutional Neural Networks
Show steps
Deepen your understanding of CNNs, a key concept in the course.
Browse courses on
Convolutional Neural Networks
Show steps
-
Find a beginner-friendly tutorial on Convolutional Neural Networks.
-
Work through the tutorial, completing all exercises and examples.
-
Research additional resources on CNNs to expand your knowledge.
Develop a custom image classification pipeline
Show steps
Gain practical experience in building and evaluating an image classification system.
Browse courses on
Image Classification
Show steps
-
Load and preprocess the COVID-19 Radiography dataset
-
Train and evaluate the ResNet-18 model
-
Create a user interface for the image classification pipeline
Complete practice exercises on PyTorch
Show steps
Gain hands-on experience with PyTorch, the framework used in the course.
Browse courses on
PyTorch
Show steps
-
Find a set of practice exercises or problems on PyTorch.
-
Attempt to solve the exercises and debug any errors.
-
Review solutions to the exercises to consolidate your understanding.
Explore advanced topics in medical image analysis
Show steps
Expand your knowledge beyond the scope of the course and explore cutting-edge techniques in medical image analysis.
Browse courses on
Medical Image Analysis
Show steps
-
Study research papers on topics such as medical image segmentation and disease classification
-
Follow online courses or tutorials on advanced medical imaging techniques
Create a poster or infographic on COVID-19 X-ray detection
Show steps
Demonstrate your understanding of the course content by creating a visual representation.
Browse courses on
Medical Imaging
Show steps
-
Gather information about COVID-19 X-ray detection techniques.
-
Design a poster or infographic that visually communicates the information.
-
Share your creation with others to educate and inform.
Read 'Deep Learning with Python' by Francois Chollet
Show steps
Enhance your understanding of deep learning concepts and expand your knowledge beyond the course material.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Obtain a copy of 'Deep Learning with Python'.
-
Set aside dedicated time for reading and studying the book.
-
Take notes and highlight important concepts while reading.
-
Attempt the exercises and examples provided in the book to reinforce your understanding.
Tutor other students in the fundamentals of image classification
Show steps
Solidify your understanding by explaining concepts to others and reinforcing your knowledge.
Browse courses on
Image Classification
Show steps
-
Volunteer to tutor at a local school or community center
-
Create online tutorials or resources for beginners in image classification
Join a study group to discuss the course material
Show steps
Engage with fellow learners, exchange ideas, and clarify any doubts you may have.
Show steps
Show steps
Show steps
- Find or create a study group with classmates
- Meet regularly to discuss the course content and work on assignments together
Organize your course notes, assignments, and study materials
Show steps
Establish a structured system for managing your course materials, aiding in effective review and retention.
Show steps
Show steps
Show steps
- Create a dedicated folder or digital notebook for your course materials.
- File and name your notes, assignments, and study materials consistently.
- Consider using different colors or tags to categorize different types of materials.
Solve practice problems on image classification
Show steps
Sharpen your problem-solving skills and reinforce your understanding of image classification concepts.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Solve coding challenges on platforms like LeetCode or HackerRank
- Participate in Kaggle competitions related to image classification
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Review Python programming
Show steps
Brush up on your Python skills to strengthen your foundation for the course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review Python syntax and data structures.
- Solve beginner-level Python coding problems.
Participate in a study group with other course participants
Show steps
Engage with peers, discuss course content, and enhance understanding through collaboration.
Show steps
Show steps
Show steps
- Find or form a study group with other course participants.
- Set regular meeting times to discuss course material and assignments.
- Take turns presenting and explaining concepts to reinforce your understanding.
- Collaborate on practice exercises and problem-solving.
Create a ResNet-18 model from scratch
Show steps
Build a solid understanding of ResNet-18 architecture and implementation.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Set up a PyTorch environment
- Define the ResNet-18 architecture
- Implement the forward and backward passes
Follow a tutorial on Convolutional Neural Networks
Show steps
Deepen your understanding of CNNs, a key concept in the course.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Find a beginner-friendly tutorial on Convolutional Neural Networks.
- Work through the tutorial, completing all exercises and examples.
- Research additional resources on CNNs to expand your knowledge.
Develop a custom image classification pipeline
Show steps
Gain practical experience in building and evaluating an image classification system.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Load and preprocess the COVID-19 Radiography dataset
- Train and evaluate the ResNet-18 model
- Create a user interface for the image classification pipeline
Complete practice exercises on PyTorch
Show steps
Gain hands-on experience with PyTorch, the framework used in the course.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Find a set of practice exercises or problems on PyTorch.
- Attempt to solve the exercises and debug any errors.
- Review solutions to the exercises to consolidate your understanding.
Explore advanced topics in medical image analysis
Show steps
Expand your knowledge beyond the scope of the course and explore cutting-edge techniques in medical image analysis.
Browse courses on
Medical Image Analysis
Show steps
Show steps
Show steps
- Study research papers on topics such as medical image segmentation and disease classification
- Follow online courses or tutorials on advanced medical imaging techniques
Create a poster or infographic on COVID-19 X-ray detection
Show steps
Demonstrate your understanding of the course content by creating a visual representation.
Browse courses on
Medical Imaging
Show steps
Show steps
Show steps
- Gather information about COVID-19 X-ray detection techniques.
- Design a poster or infographic that visually communicates the information.
- Share your creation with others to educate and inform.
Read 'Deep Learning with Python' by Francois Chollet
Show steps
Enhance your understanding of deep learning concepts and expand your knowledge beyond the course material.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of 'Deep Learning with Python'.
- Set aside dedicated time for reading and studying the book.
- Take notes and highlight important concepts while reading.
- Attempt the exercises and examples provided in the book to reinforce your understanding.
Tutor other students in the fundamentals of image classification
Show steps
Solidify your understanding by explaining concepts to others and reinforcing your knowledge.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Volunteer to tutor at a local school or community center
- Create online tutorials or resources for beginners in image classification
Career center
Learners who complete Detecting COVID-19 with Chest X-Ray using PyTorch will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, deploy, and maintain machine learning models. They use their knowledge of programming, statistics, and algorithms to build models that can automate tasks, make predictions, and detect patterns in data. This course provides a foundation in using PyTorch to develop a machine learning model for image classification, which is a fundamental skill for Machine Learning Engineers.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts collect, analyze, and interpret data to improve the quality and efficiency of healthcare delivery. They use their knowledge of healthcare and data analysis to identify trends and patterns in healthcare data. This course provides a foundation in using Python and PyTorch to develop a deep learning model for medical image analysis, which could be helpful for Healthcare Data Analysts working on projects related to COVID-19.
Radiologist
Radiologist
Radiologists interpret medical images, such as X-rays, CT scans, and MRIs, to diagnose and treat diseases. They use their knowledge of anatomy and physiology to identify abnormalities in medical images. This course may be helpful for Radiologists who want to build a foundation in using deep learning for medical image analysis.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to identify trends and patterns. They use their knowledge of statistics and programming to develop tools and techniques for data analysis. This course provides a foundation in using Python to analyze medical imaging data, which could be helpful for Data Analysts working in the healthcare industry.
Medical Physicist
Medical Physicist
Medical Physicists use their knowledge of physics and medicine to develop and use medical imaging technologies. They work with physicians to optimize imaging protocols and ensure that imaging equipment is safe and effective. This course could be helpful for Medical Physicists who want to build a foundation in using deep learning for medical image analysis.
Nuclear Medicine Physician
Nuclear Medicine Physician
Nuclear Medicine Physicians use radioactive substances to diagnose and treat diseases. They use their knowledge of nuclear medicine and physiology to interpret medical images and develop treatment plans. This course may be helpful for Nuclear Medicine Physicians who want to build a foundation in using deep learning for medical image analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages to create code that meets the needs of users. This course provides a foundation in using Python and PyTorch to develop a deep learning model.
Pathologist
Pathologist
Pathologists examine cells and tissues under a microscope to diagnose and treat diseases. They use their knowledge of pathology and histology to identify abnormalities in cells and tissues. This course may be helpful for Pathologists who want to build a foundation in using deep learning for medical image analysis.
Medical Imaging Analyst
Medical Imaging Analyst
Medical Imaging Analysts analyze images, such as X-rays, CT scans, and MRIs, to identify abnormalities and make diagnoses. This course may be helpful for Medical Imaging Analysts who want to build a foundation in using deep learning to analyze medical images.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease outbreaks and develop strategies to prevent and control them. They use their knowledge of epidemiology and public health to design and conduct studies, and then analyze the results to generate new knowledge. This course may be helpful for Epidemiologists who want to build a foundation in using deep learning for medical image analysis.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology, medicine, and public health. They use their knowledge of statistics and biology to design and conduct studies, and then analyze the results to generate new knowledge. This course may be helpful for Biostatisticians who want to build a foundation in using deep learning for medical image analysis.
Microbiologist
Microbiologist
Microbiologists study microorganisms, such as bacteria, viruses, and fungi. They use their knowledge of microbiology and molecular biology to identify and characterize microorganisms. This course may be helpful for Microbiologists who want to build a foundation in using deep learning for medical image analysis.
Virologist
Virologist
Virologists study viruses and the diseases they cause. They use their knowledge of virology and molecular biology to identify and characterize viruses. This course may be helpful for Virologists who want to build a foundation in using deep learning for medical image analysis.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including medicine, engineering, and computer science. They use their knowledge of scientific methods to design and conduct experiments, and then analyze the results to generate new knowledge. This course could be helpful for Research Scientists who want to build a foundation in using deep learning for medical image analysis.
Data Scientist
Data Scientist
A Data Scientist analyzes data in order to make critical business decisions. They develop algorithms to evaluate data to detect patterns and trends, and then analyze this information to generate insights. This course could be helpful for those looking to build a foundation in using Python to analyze medical imaging data.
For more career information including salaries, visit:
OpenCourser.com/course/oohxu4/detecting
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Detecting COVID-19 with Chest X-Ray using PyTorch.
Save
Comprehensive guide to deep learning, covering the theoretical underpinnings as well as practical implementation. It is widely used as a textbook in graduate-level machine learning courses.
Provides a comprehensive overview of machine learning with Python, covering a wide range of topics from data preprocessing and feature engineering to model evaluation and deployment.
Provides a comprehensive overview of deep learning with Python, covering a wide range of topics from the basics of neural networks to advanced topics such as generative adversarial networks and reinforcement learning.
Save
Provides a comprehensive introduction to computer vision, covering a wide range of topics from image formation and processing to object detection and recognition.
Save
Provides a theoretical foundation for machine learning, covering a wide range of topics from probability and statistics to Bayesian inference and kernel methods.
Provides a comprehensive overview of computer vision with Python, covering a wide range of topics from image processing and feature extraction to object detection and recognition.
Provides a comprehensive overview of deep learning for medical image analysis, covering a wide range of topics from image segmentation and registration to image classification and detection.
Provides a comprehensive overview of machine learning for healthcare, covering a wide range of topics from data preprocessing and feature engineering to model evaluation and deployment.
Save
Provides a comprehensive overview of artificial intelligence in healthcare, covering a wide range of topics from the history of AI in healthcare to the ethical and legal challenges of using AI in healthcare.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/oohxu4/detecting
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Develops skills in working with computer vision
Appropriate for learners who are familiar with Python, convolutional neural networks, and gradient descent
Employs a practical, hands-on approach to reinforce learning
Focuses on implementation rather than advanced theoretical concepts
Utitlizes the ResNet-18 model for image classification
Emphasizes the importance of practical, hands-on learning
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