Résumé: Medical data processing and analysis using machine learning algorithms are a prominent research topic these days. To obtain high performances, most state-of-the-art models must be trained on a large number of labeled datasets. However, manually collecting a large-scale real-world medical data is expensive due to the issues like privacy, security, and reliability. Moreover, the collected samples may be incomplete, i.e., some important data items are missing, which has a significant impact on the performance of the machine learning algorithms, especially deep learning models. In this paper, we present a strategy that uses the idea of adversarial learning to augment the real-world medical samples with the incomplete issue to obtain better performance in predicting patient states. Rather than supplying the incomplete data sample with extra instance-level information as in existing data-complementation methods, our method aims to achieve the complementary effect in the feature level without consuming human effort. The method receives both high-quality data and low-quality data (with serious incomplete issue) to learn a comprehensive feature space where the incomplete samples can be complemented with the knowledge transferred from the high-quality samples by the adversarial learning scheme. From the perspective of feature representations, our method can alleviate the incomplete issue of the low-quality data, which enhances the model performance in the end task. Experiments show that our method works well on a real-world medical dataset, which collected for assessment of pelvic organ prolapse. When compared to machine learning approaches frequently employed in medical data analysis, our approach shows a significant improvement of up to 10%.

Langue: Anglais