The text provides a comprehensive guide to MRI Analysis through Deep Learning models in PyTorch. It introduces the author’s AI research on brain tumor grade classification using DL models and highlights challenges in using medical image data with DL models. It covers CNN fundamentals, MRI data preparation, and PyTorch model setup. The guide also includes information on the BraTS dataset and practical application of ResNet18 for image classification.
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A Comprehensive Guide to MRI Analysis through Deep Learning models in PyTorch
Introduction
Carla Pitarch, a PhD candidate in AI, shares practical knowledge on developing an automated brain tumor grade classification system using Deep Learning models and MRI data.
Background
Delve into the fundamental aspects of Convolutional Neural Networks (CNNs) and Magnetic Resonance Imaging (MRI) to understand the intersection of MRI and Deep Learning.
Image Classification
Learn how to effectively utilize the ResNet18 architecture for classifying MRI images into low or high-grade tumors.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Dealing with MRI and Deep Learning with Python
- Towards Data Science – Medium
- Twitter – @itinaicom
