Explainable AI

Image Segmentation you own this product

This project is part of the liveProject series Transformers and Explainable AI for Computer Vision
prerequisites
intermediate Python (particularly TensorFlow or PyTorch) • intermediate knowledge of image segmentation principles • Intermediate knowledge of image visualization
skills learned
set up training, test, and validation datasets with biomedical (MRI) images • train and evaluate Segformer and Maskformer models for brain tumor segmentation
Anuradha Kar
1 week · 2-4 hours per week · INTERMEDIATE
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Look inside

In this liveProject, you'll pioneer the development of cutting-edge MRI segmentation algorithms using transformer architecture for computer vision company VisionSys. Manual segmentation is labor-intensive and expensive, so you’ll be developing a custom model that can do it for you. You'll train and evaluate SegFormer and MaskFormer models to identify brain tumor regions with over 90% accuracy. With Python tools like Hugging Face Transformers and Google Colab's GPU computing resources, you'll create pipelines, preprocess data, and showcase sample predictions and quantitative results.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Anuradha Kar
Anuradha Kar is a researcher at the Institut Pasteur in Paris, working on deep learning applications in drug discovery. Before this, she worked at the Paris Brain Institute on applying attention-based deep learning models to understanding the evolution of Alzheimer's disease and at École normale supérieure de Lyon in France on deep learning-based analysis of 3D bio-image datasets. She has a Ph.D. in electrical engineering from the National University of Ireland, Galway. In 2021, she published a liveProject series with Manning Publications titled Transfer Learning for Dicom Image Classification.

prerequisites

This liveProject series is aimed at intermediate-level Python programmers who already know the basics of deep learning and computer vision.


TOOLS
  • Intermediate Python
  • Intermediate Jupyter Notebook
  • Intermediate TensorFlow
  • Intermediate PyTorch
  • Intermediate OpenCV

TECHNIQUES
  • Intermediate levels of deep learning and image classification
  • Intermediate levels of data science

you will learn

This liveProject will hone your skills in image segmentation, from creating pipelines to evaluating your results.


  • Process data and generate data loaders for image segmentation.
    • - Create Hugging Face-compatible image segmentation datasets and upload them to the Hugging Face Hub.
  • Utilize Hugging Face Transformer libraries for computer vision tasks.
  • Fine-tune two state-of-the-art Transformer models for image segmentation.
  • Develop pipelines for training and evaluating Transformer models for MRI segmentation.

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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  • Image Segmentation project for free

team

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  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Image Segmentation project for free