Cancer Image Detection With PyTorch (Part 3 iBest Workshop)
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Discover how deep learning and computer vision techniques are used to identify metastatic cancer in digital pathology scans. Leverage pre-trained CNNs and transfer learning for improved model performance.
At 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.
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.
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