jonahanton/SSL_medicalimaging
Codebase for Imperial MSc AI Group Project: How Well Do Self-Supervised Models Transfer to Medical Imaging?
This project helps medical imaging specialists understand how well different self-supervised models perform when applied to various medical image classification tasks. It takes different types of medical image datasets (like X-rays or retinal scans) and pre-trained models as input, then evaluates their accuracy on classification problems. Radiologists, ophthalmologists, and other medical professionals who work with diagnostic images can use this to compare different AI approaches for image analysis.
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Use this if you are a medical imaging professional or researcher wanting to compare the effectiveness and generalizability of different AI models for classifying medical images.
Not ideal if you need a plug-and-play tool for immediate diagnostic assistance, as this is a research codebase for model comparison, not a deployed application.
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Dec 08, 2022
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