YalaLab/pillar-finetune
Finetuning framework for Pillar medical imaging models.
This framework helps radiologists and clinical researchers adapt advanced medical imaging AI models (Pillar models) for specific research studies or patient populations. You input your collection of medical images (like chest CT scans in DICOM or NIfTI format) and a CSV file describing your dataset, and it outputs a specialized AI model trained to better interpret those images for your particular needs. This is used by medical imaging researchers, clinicians, and data scientists working with diagnostic imaging.
Use this if you need to customize an existing Pillar medical imaging AI model to improve its accuracy or relevance for a unique medical imaging dataset or research question.
Not ideal if you are looking for a pre-trained, out-of-the-box diagnostic tool without needing to train it on your own data.
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Language
Python
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Last pushed
Feb 26, 2026
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