A step-by-step guide to implementing a deep learning semantic segmentation pipeline on mammograms in TensorFlow 2

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Article Structure

This article is Part 3 of a 3-part series that walks through how I tackled a deep learning project of identifying mass abnormalities in mammogram scans using an image segmentation model_._As a result of breaking down the project in detail, this serves as a comprehensive overview of one of the core problems in computer vision — semantic segmentation, as well as a deep dive into the technicalities of executing this project in TensorFlow 2.

Part 1:

• Problem statement.
• What is semantic segmentation.
• Guide to downloading the dataset.
• What you’ll find in the dataset.
• Unravelling the nested folder structure of the dataset.
• Data exploration.

Part 2:

• Image preprocessing pipeline overview.
• General issues with the raw mammograms.
• Deep dive into raw mammogram’s preprocessing pipeline.
• Deep dive into corresponding mask’s preprocessing pipeline.

Part 3:

• Introducing the VGG-16 U-Net model.
• Implementing the model in TensorFlow 2.
• Notes on training the model.
• Results and post analysis.
• Wrapping up.

GitHub Repository

The code for this project can be found on my Github in this repository.

#computer-vision #transfer-learning #deep-learning #tensorflow #machine-learning