mazurowski-lab/radiologyintrinsicmanifolds
(MICCAI 2022) Code for "The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning" (intrinsic dimensions)
This tool helps researchers in medical imaging understand the underlying complexity of radiology datasets. By analyzing medical image datasets (like X-rays or MRIs), it calculates their 'intrinsic dimension', which indicates how complex the image features are. Researchers can use this to predict how well a deep learning model might perform on that specific dataset, guiding them in developing more effective AI for medical diagnosis.
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Use this if you are a medical imaging researcher or data scientist evaluating the suitability of radiology datasets for deep learning and want to understand their inherent complexity and potential impact on model generalization.
Not ideal if you are a clinician looking for a diagnostic tool or a developer seeking a ready-to-use deep learning model for medical image analysis.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Jun 27, 2024
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