SwinUNeCCt: bidirectional hash-based agent transformer for cervical cancer MRI image multi-task learning

作者：Chongshuang Yang, Zhuoyi Tan, Yijie Wang, Ran Bi, Tianliang Shi, Yang Jing, Chao Huang, Peng Jiang, Xiangyang Fu · 发表于：Scientific Reports · 年份：2024 · DOI：10.1038/s41598-024-75544-5 · 被引用次数：2 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Medical Imaging and Analysis

Cervical cancer is the fourth most common malignant tumor among women globally, posing a significant threat to women's health. In 2022, approximately 600,000 new cases were reported, and 340,000 deaths occurred due to cervical cancer. Magnetic resonance imaging (MRI) is the preferred imaging method for diagnosing, staging, and evaluating cervical cancer. However, manual segmentation of MRI images is time-consuming and subjective. Therefore, there is an urgent need for automatic segmentation models to identify cervical cancer lesions in MRI scans accurately. All MRIs in our research are from cervical cancer patients diagnosed by pathology at Tongren City People's Hospital. Strict data selection criteria and clearly defined inclusion and exclusion conditions were established to ensure data consistency and accuracy of research results. The dataset contains imaging data from 122 cervical cancer patients, with each patient having 100 pelvic dynamic contrast-enhanced MRI scans. Annotations were jointly completed by medical professionals from Universiti Putra Malaysia and the Radiology Department of Tongren City People's Hospital to ensure data accuracy and reliability. Additionally, a novel computer-aided diagnosis model named SwinUNeCCt is proposed. This model incorporates (i) A bidirectional hash-based agent multi-head self-attention mechanism, which optimizes the interaction between local and global features in MRI, aiding in more accurate lesion identification. (ii) Reduced com...