Image Segmentation you own this product

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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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.

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.


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

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


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