Image Classification you own this product

intermediate Python, particularly TensorFlow or PyTorch • intermediate knowledge of image classification principles • Intermediate knowledge of image visualization
skills learned
set up training, test, and validation datasets with biomedical (MRI) images • train and evaluate a vision transformer model for brain tumor classification
Anuradha Kar
1 week · 2-4 hours per week · INTERMEDIATE

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In this liveProject, you’ll join BrainAI’s MRI data analysis team. BrainAI needs you to develop a state-of-the-art AI module utilizing vision transformer models to detect brain tumors with exceptional accuracy. Armed with Python tools like Hugging Face Transformers, PyTorch, and more, you'll detect the presence or absence of tumors within human-brain MRI datasets. With Google Colab's GPU computing resources, you'll utilize deep learning to try and achieve a 95%+ accuracy rate.

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.

  • Intermediate Python
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  • Intermediate OpenCV

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


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