Brain Tumor Detection with YOLO: Self-Supervised Pretraining & Fine-tuning Using LightlyTrain

Learn how to build a powerful brain tumor classification system using YOLO and LightlyTrain's self-supervised learning approach. In this comprehensive tutorial, we demonstrate how to leverage unlabeled medical imaging data to create a robust foundation model before fine-tuning with limited labeled data.

What You'll Learn:
1. Foundations of computer vision: classification, detection, localization, and segmentation
2. Why self-supervised learning is revolutionizing medical imaging analysis
3. Setting up LightlyTrain for pretraining on unlabeled brain scans
4. Fine-tuning a YOLO model for precise tumor detection and classification
5. Evaluation strategies and performance metrics for medical imaging models

Discover how LightlyTrain can reduce your labeling costs by up to 80% while improving model accuracy on domain-specific medical imaging tasks. This approach is perfect for researchers and practitioners working with limited labeled datasets in healthcare applications.
Code and resources available in the description. Subscribe for more tutorials on advanced computer vision techniques for medical imaging!

LightlyTrain page: https://www.lightly.ai/lightlytrain?u...
LightlyTrain Github: https://github.com/lightly-ai/lightly...
LightlyTrain Docs: https://docs.lightly.ai/train/stable/...
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Google Colab Notebook: https://colab.research.google.com/dri...

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#MedicalAI #ComputerVision #SelfSupervisedLearning #YOLO #TumorDetection #MachineLearning #Python #DeepLearning #MedicalImaging #Healthcare #LightlyTrain #CodeWithPrince

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