Biomedical image classification made easier thanks to transfer and semi-supervised learning. (January 2021)

• Record Type: Journal Article
• Authors: Inés, A.; Domínguez, C.; Heras, J.; Mata, E.; Pascual, V.
• Is Part Of: Computer methods and programs in biomedicine. Volume 198(2021)
• Publication Date: 2021-01
• Subjects: AutoML -- Image classification -- Semi-Supervised learning -- Transfer-learning -- Benchmark; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105782
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new semi-supervised learning method based on the notions of transfer learning and data and model distillation is presented. We present ATLASS, the first AutoML tool for constructing image classification models that incorporates semi-supervised learning. ATLASS allows non-expert users to easily construct their own image classification models using our method. Abstract: Background and objectives: Deep learning techniques are the state-of-the-art approach to solve image classification problems in biomedicine; however, they require the acquisition and annotation of a considerable volume of images. In addition, using deep learning libraries and tuning the hyperparameters of the networks trained with them might be challenging for several users. These drawbacks prevent the adoption of these techniques outside the machine-learning community. In this work, we present an Automated Machine Learning (AutoML) method to deal with these problems. Methods: Our AutoML method combines transfer learning with a new semi-supervised learning procedure to train models when few annotated images are available. In order to facilitate the dissemination of our method, we have implemented it as an open-source tool called ATLASS. Finally, we have evaluated our method with two benchmarks of biomedical image classification datasets. Results: Our method has been thoroughly tested both with small datasets and partially annotated biomedical datasets; and, it outperforms, both in terms of speed and accuracy, the existing AutoML tools when working with small datasets; and, might improve the accuracy of models up to a 10% when working with partially annotated datasets. Conclusions: The work presented in this paper allows the use of deep learning techniques to solve an image classification problem with few resources. Namely, it is possible to train deep models with small, and partially annotated datasets of images. In addition, we have proven that our AutoML method outperforms other AutoML tools both in terms of accuracy and speed when working with small datasets.