Deep learning approaches often require huge datasets to achieve good generalization. This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations. For such sensitive tasks it is also important to provide the confidence in the predictions. Here, we propose a way to learn and use probabilistic labels to train accurate and calibrated deep networks from relatively small datasets. We observe gains of up to 22% in the accuracy of models trained with these labels, as compared with traditional approaches, in three classification tasks: diagnosis of hip dysplasia, fatty liver, and glaucoma. The outputs of models trained with probabilistic labels are calibrated, allowing the interpretation of its predictions as proper probabilities. We anticipate this approach will apply to other tasks where few training instances are available and expert knowledge can be encoded as probabilities.
@article{arxiv.2102.06164,
title = {Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels},
author = {Roberto Vega and Pouneh Gorji and Zichen Zhang and Xuebin Qin and Abhilash Rakkunedeth Hareendranathan and Jeevesh Kapur and Jacob L. Jaremko and Russell Greiner},
journal= {arXiv preprint arXiv:2102.06164},
year = {2021}
}
Comments
To appear in the Proceedings of the 24 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego,California, USA. PMLR: Volume 130