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Is it possible to release your best weights? Our team will use them for research rather than commercial purposes, and will appropriately cite your work in the paper.
Hi, we are interested in the Deep AUC Maximization (DAM) model from the paper "Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification". The model achieves 0.93 AUC on CheXpert.
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optmai commentedon Sep 24, 2024
What task are you referring to?
LiGuo12 commentedon Sep 24, 2024
Hi, we are interested in the Deep AUC Maximization (DAM) model from the paper "Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification". The model achieves 0.93 AUC on CheXpert.
optmai commentedon Sep 25, 2024
Thank you for your interest! The result is actually an ensemble of several models trained using deep AUC maximization. You can follow the tutorial here https://github.com/Optimization-AI/LibAUC/blob/main/examples/05_Optimizing_AUROC_Loss_with_DenseNet121_on_CheXpert.ipynb to train a model. You can use different losses/optimizers available in our library to train different models and get an ensemble. The original models for achieving 0.93 AUC on CheXpert were not available anymore.