What Is an Edge Case?
January 31, 2024
Article
This course is part of AI for Medicine Specialization
Instructors: Pranav Rajpurkar
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AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required!
This program will give you practical experience in applying cutting-edge machine learning techniques to concrete problems in modern medicine: - In Course 1, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders. - In Course 2, you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis. - In Course 3, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports. These courses go beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. As a learner, you will be set up for success in this program if you are already comfortable with some of the math and coding behind AI algorithms. You don't need to be an AI expert, but a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level will be essential. If you are relatively new to machine learning or neural networks, we recommend that you first take the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng. The demand for AI practitioners with the skills and knowledge to tackle the biggest issues in modern medicine is growing exponentially. Join us in this specialization and begin your journey toward building the future of healthcare.
By the end of this week, you will practice classifying diseases on chest x-rays using a neural network.
20 videos3 readings1 assignment1 programming assignment1 app item4 ungraded labs
By the end of this week, you will practice implementing standard evaluation metrics to see how well a model performs in diagnosing diseases.
10 videos1 reading1 assignment1 programming assignment1 ungraded lab
By the end of this week, you will prepare 3D MRI data, implement an appropriate loss function for image segmentation, and apply a pre-trained U-net model to segment tumor regions in 3D brain MRI images.
10 videos5 readings1 assignment1 programming assignment3 ungraded labs
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
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1,985 reviews
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Showing 3 of 1985
Reviewed on May 5, 2020
Last assignment may be divided into two files... as it is becoming heavy to solve and even upload.Rest is fine. Congratulation on designing such a pin pointed course in Medical Diagnosis
Reviewed on Nov 29, 2024
The instructor is excellent. I knocked it down a star for the finicky auto-grader. Would love to have had a fourth week that showed how to re-train a previously trained system.
Reviewed on Jun 3, 2020
Use cases selected were really nice, Videos should carry more detail technical aspects and could be bit more lengthy and Assignments should consider multiple options to solve given problem
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