Data Constraints? Not anymore: Image-Only Learning with Cross-Institutional Applicability for Ultrasound Video Segmentation

In video analysis, existing approaches achieve excellent performance under preset conditions, but the scarcity of training videos limits deep learning development. To address this, this paper introduces two external resources to alleviate the video shortage: image data and cross-institutional data. We propose a novel method, IOLCIA, which combines image-only learning and cross-institutional learning to maximize the use of image resources. Additionally, it leverages the differences and similarities in data from various institutions to enhance feature learning. Comparison experiments show that our method outperforms models trained solely on images, with only a slight decrease in effectiveness compared to those trained on videos.