Unsupervised domain adaptive myocardial infarction MRI classification diagnostics model based on target domain confused sample resampling

• Published in: Computer methods and programs in biomedicine 2022-11, Vol.226, p.107055-107055, Article 107055
• Main Authors: Xie, Weifang, Ding, Yuhan, Liao, Zhifang, Wong, Kelvin K.L.
• Format: Article
• Language: English
• Subjects: CardiacCN; Confrontational learning; Domain adaptation; Image classification; Myocardial infarction
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2022.107055

•An unsupervised site-responsive myocardial infarction MRI classification method relying on adversarial teaching relevant to the target domain confusion samples interpolation is proposed for the confusing specimens with conflicting target domain classification tasks to mine the classification understanding of source domain photographs effectively.•When the temperature-controlled length hyper-parameter rl falls in the range of 5–30, the classification accuracy of the CardiacCN model on the target domain does not fluctuate considerably.•The average target domain myocardial infarction MR images classification accuracy is improved by nearly 1.2%.•The model can effectively improve the efficiency and accuracy of image classification. Inefficient circulatory system due to blockage of blood vessels leads to myocardial infarction and acute blockage. Myocardial infarction is frequently classified and diagnosed in medical treatment using MRI, yet this method is ineffective and prone to error. As a result, there are several implementation scenarios and clinical significance for employing deep learning to develop computer-aided algorithms to aid cardiologists in the routine examination of cardiac MRI. This research uses two distinct domain classifiers to address this issue and achieve domain adaptation between the particular field and the specific part is a problem Current research on environment adaptive systems cannot effectively obtain and apply classification information for unsupervised scenes of target domain images. Insufficient information interchange between specific domains and specific domains is a problem. In this study, two different domain classifiers are used to solve this problem and achieve domain adaption. To effectively mine the source domain images for classification understanding, an unsupervised MRI classification technique for myocardial infarction called CardiacCN is proposed, which relies on adversarial instructions related to the interpolation of confusion specimens in the target domain for the conflict of confusion specimens for the target domain classification task. The experimental results demonstrate that the CardiacCN model in this study performs better on the six domain adaption tasks of the Sunnybrook Cardiac Dataset (SCD) dataset and increases the mean target area myocardial infarction MRI classification accuracy by approximately 1.2 percent. The classification performance of the CardiacCN model on the target domain does not vary notic