Attention-based Two Stage Deep Learning Model for the Segmentation and Classification of Pelvic and Sacral Tumors on Routine MRI

Early detection and correct diagnosis are key to adequate and successful treatment of PSTs. Based on multi-sequence MRI images, physician’s labeling, segmentation model, and clinical features, six classification models were built. The highest scoring model (model 6) achieved 0.836 AUC, 0.781 ACC in the prospective test set, which was comparable to that of senior residents and junior resident. However, the diagnosing time of DL model is significantly shorter than physicians. Our attention-based two stage DL model allowed the accurate segmentation and classification of benign and malignant PSTs without enhanced MRI and may thus facilitate diagnosis.