<p>Analyzing cardiac pathology using image-derived features is a complex undertaking requiring a substantial amount of labeled data. Semi-supervised learning requires a small amount of labeled data and a large amount of unlabeled data. However, most semi-supervised approaches are not robust for ventricular segmentation to extract shape-based information; hence, they have limited segmentation accuracy compared with supervised learning. Therefore, we proposed a dual-path copy-paste segmentation network in a mean Teacher architecture to learn knowledge from labeled images and transfer it to unlabeled images in a dual-path learning manner. This effectively reduces the empirical data distribution gap and learns the semantic information from labeled data in both the inward and outward directions for shape-based feature extraction. We also extracted the motion parameters from two input image frames (2D + ) of the same slice from a cine magnetic resonance image (MRI). After that, we fused the segmentation mask of the myocardium wall and motion parameters to generate the dynamic characteristics of the time-series for cardiac pathology classification. In our evaluation, we compared the segmentation outcomes of our method with a state-of-the-art semi-supervised approach using the automatic cardiac diagnosis challenge (ACDC) dataset. We assessed two different ratios of labeled data, at 5% and 10%, respectively. Our method delivered impressive results, achieving an average dice score of 87.85% and 88.66% under these conditions. Moreover, our model shows a promising classification accuracy of 97% for the training set and 96% for the testing set. The proposed method was trained end-to-end, demonstrating a general framework for automatic cardiac pathology classification from cine MRI in a clinical setting.</p>