{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatio-temporal-collaborative-multiple-stream","title":"Spatio-temporal collaborative multiple-stream transformer network for liver lesion classification on multiple-sequence magnetic resonance imaging","arxiv_id":null,"date":"2025-02-15","proceeding":"Engineering Applications of Artificial Intelligence 2025 2","authors":["Shuangping Huang","Zinan Hong","Bianzhe Wu","Jinglin Liang","Qinghua Huang"],"abstract":"Accurate identification of focal liver lesions is essential for determining the appropriate therapeutic approach in clinical practice. Magnetic resonance imaging (MRI) is a valuable technology for precise classification, revealing diverse physical and biological details of lesions. However, due to the wide variety and morphological variability of the lesions, unsystematic mixing analysis of multiple-sequences MRI may cause aliasing of lesion information, obscuring the inter-tissue relationships between various imaging sequences and impeding a comprehensive diagnosis. In this paper, we proposed a Spatio-Temporal Collaborative Multiple-Stream Transformer Network that simultaneously considers spatial contrast and temporal variations to obtain detailed information on anatomical structures and tissue dynamics, effectively organizing and utilizing multiple-sequence MRI for analysis. Specifically, multiple-sequence MRI is first grouped into multiple streams based on MRI diagnostic characteristics. To reduce the interference of redundancy across multiple streams, we design a bottleneck bridge structure for spatial information aggregation. Additionally, we adopt a bidirectional Long Short-Term Memory to simulate radiologists observing the vascular morphology and hemodynamics in lesion sites from contrast-enhanced sequences. Experiments conducted on public MRI dataset, which includes seven categories of focal liver lesions from 498 patients, demonstrate that our framework achieves state-of-the-art performance, with an accuracy of 85.6%, a precision of 87.4%, a recall of 84.2%, an F1-score of 85.3%, and an Area Under the Curve of 97.1%. The experimental results indicate that the network performs well in predicting focal liver lesions, advancing the application of precision medicine.","url_abs":"https://www.sciencedirect.com/science/article/abs/pii/S095219762402092X","url_pdf":"https://www.sciencedirect.com/science/article/abs/pii/S095219762402092X","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spatio-temporal-collaborative-multiple-stream","repo_url":"https://github.com/zaynah91124/STM-Former","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"lesion-classification","task_name":"Lesion Classification"}],"methods":[{"method_slug":"adopt","method_name":"ADOPT"},{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}