Image Segmentation you own this product

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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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 Postdoctoral researcher at École normale supérieure de Lyon, and works in collaboration with the research institutes INRAE and INRIA in France. Her current research is on the application of deep learning algorithms for deriving quantitative information from microscopy image datasets. This is used by biologists to analyze cellular developmental processes in plants and animals. She has a PhD in electrical engineering from the National University of Ireland, Galway. Her research centers on vision sensors, artificial intelligence and computer vision. She has published on deep learning, human-computer interactions and sensor evaluation techniques.

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

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