yuval6957/SIIM-Transformer
Yuval and nosound models and write-up for Kaggle's competition "SIIM-ISIC Melanoma Classification"
This project offers a robust framework for classifying melanoma from medical images, helping medical researchers or AI practitioners in healthcare. It takes patient metadata and dermatology images (DICOM and JPG) as input, processes them through specialized neural networks, and outputs predictions on whether a lesion is malignant. This is ideal for those focused on improving diagnostic accuracy in dermatological imaging.
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Use this if you need to build or evaluate a highly accurate deep learning model for classifying melanoma from skin lesion images, integrating both visual and patient data.
Not ideal if you are looking for a plug-and-play diagnostic tool without needing to engage with model training or fine-tuning.
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Last pushed
Sep 10, 2020
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