Offered By: IBMSkillsNetwork
Cancer Image Detection With PyTorch (Part 3 iBest Workshop)
This project uses deep learning in PyTorch and computer vision techniques to develop an algorithm to identify metastatic cancer in small image patches obtained from larger digital pathology scans. The project's objectives are to set up the necessary environment, install and import required libraries, and perform data preparation for the model training. The project leverages pre-trained Convolutional Neural Networks (CNNs) and transfer learning to improve the model's performance.
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Guided Project
Artificial Intelligence
159 EnrolledAt a Glance
This project uses deep learning in PyTorch and computer vision techniques to develop an algorithm to identify metastatic cancer in small image patches obtained from larger digital pathology scans. The project's objectives are to set up the necessary environment, install and import required libraries, and perform data preparation for the model training. The project leverages pre-trained Convolutional Neural Networks (CNNs) and transfer learning to improve the model's performance.
A Look at the Project Ahead
- Gain knowledge and understanding of computer vision techniques and their application in medical imaging.
- Learn how to use deep learning algorithms for image classification tasks with PyTorch.
- Understand the concept of transfer learning and how it can be used to improve model performance with limited data.
- Gain experience in data preparation techniques for deep learning models, including data loading, augmentation, and normalization.
What You'll Need
Estimated Effort
45 Min
Level
Beginner
Industries
Healthcare
Skills You Will Learn
Artificial Intelligence, Machine Learning, Python, PyTorch
Language
English
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.
Read moreSina 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.
Read moreAlice Rueda
Postdoctoral Fellow
Alice is a postdoctoral fellow and AI Lead at the Interventional Psychiatry Program, St. Michael’s Hospital and iBEST Trainee Lead. Alice completed her doctoral degree in electrical engineering from Toronto Metropolitan University (formerly Ryerson University), Toronto, ON in 2021. After working in the industry for more than a decade, I decided to pursuit my doctoral degree in 2016. I received a bachelor degree in electrical engineering and a master degree in electrical and computer engineering from the University of Manitoba, Winnipeg, MB in 1994 and 1999, respectively. I was awarded an (honoris causa) Doctor of Laws degree from Brock University, St. Catharines, ON in 2020. I specialize in signal processing and applications of machine learning. I am currently serving as the Secretary for the IEEE Signal Processing Toronto Chapter, an affiliated member of the IEEE Machine Learning for Signal Processing, and a reviewer for IEEE conferences. Alice had also served as the Director of Machine Learning at Aggregate Intellect in 2022.
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