lilygeorgescu/MAE-medical-anomaly-detection
Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images
This helps radiologists and medical researchers automatically identify unusual regions in medical images like MRI or CT scans. You provide a collection of 'healthy' scans, and the system learns what normal looks like. It then processes new scans, highlighting areas that deviate from this learned normal structure, outputting an assessment of potential anomalies. This is ideal for medical professionals who need to efficiently screen large volumes of medical imagery for deviations.
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Use this if you need to detect anomalies in medical images without having many examples of abnormal cases for training.
Not ideal if you already have a large, labeled dataset of both healthy and abnormal medical images, or if you are not working with medical image data.
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Language
Python
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Last pushed
Aug 15, 2023
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