This project involves using deep learning 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. The dataset used for this project comprises Positive Cell Adenocarcinoma Margin (PCAM) images. The project involves loading and training the model on this dataset, with the ultimate goal of accurately identifying metastatic cancer in digital pathology scans.

A Look at the Project Ahead

After finishing this project you will be able to:

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

Basic Python programming skill, basic machine learning task, and a browser.

Cancer Image Detection With PyTorch (Part 3 iBest Workshop)

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