AIRMEC/HECTOR
Multimodal deep learning to predict distant recurrence-free probability from digitized H&E tumour slide and tumour stage.
This project helps medical professionals, specifically pathologists and oncologists, predict the likelihood of distant recurrence in endometrial cancer patients. It takes digitized images of H&E-stained tumor slides and tumor stage information, then outputs a probability score for recurrence-free survival. This tool is designed for clinical researchers and medical practitioners specializing in oncology to aid in patient risk assessment.
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Use this if you need a deep learning model to predict endometrial cancer recurrence risk based on histopathology images and tumor stage.
Not ideal if you are looking for a diagnostic tool for other cancer types or if you don't have access to digitized H&E tumor slides and associated tumor stage data.
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44
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4
Language
Python
License
—
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Last pushed
Dec 16, 2024
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