xingbpshen/nested-diffusion
[IEEE Trans. Med. Imaging] The official implementation of the paper "Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles".
This project helps medical professionals and researchers improve the reliability of automated medical image diagnoses. It takes medical images, like X-rays or skin scans, and processes them to provide more accurate classifications even when the images are noisy or corrupted. Radiologists, dermatologists, or clinical researchers using AI for diagnostic support would benefit from this tool.
Use this if you need to build or evaluate AI models for medical image classification that remain highly accurate and trustworthy, even with imperfect real-world imaging data.
Not ideal if you are looking for an out-of-the-box diagnostic tool; this project provides the underlying methodology for building more robust classification systems.
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Language
Python
License
MIT
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Last pushed
Dec 11, 2025
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