Abstract
Comprehensive vertebrae tumor diagnosis (vertebrae recognition and vertebrae tumor diagnosis from MRI images) is crucial for tumor screening and preventing further metastasis. However, this task has not yet been attempted due to challenges caused by various tumor appearance, non-tumor diseases with similar appearance, irrelevant interference information, as well as diverse MRI image field of view (FOV) and/or characteristics. We purpose a discriminative dictionary-embedded network (DECIDE) that contains an elaborated enhanced-supervision recognition network (ERN) and a discerning diagnosis network (DDN). Our ERN creatively designs projection-guided dictionary learning to leverage projections of angular point coordinates onto multiple observation axes for enhanced supervision and discriminability of different vertebrae. DDN integrates a novel label consistent dictionary learning layer into a classification network to obtain more discerning sparse codes for diagnosing performance improvement. DECIDE is trained and evaluated using a very challenging dataset consisted of 600 MRI images; the evaluation results show that DECIDE achieves high performance in both recognition (accuracy: 0.928) and diagnosis (AUC: 0.96) tasks.
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Zhao, S., Chen, B., Chang, H., Wu, X., Li, S. (2020). Discriminative Dictionary-Embedded Network for Comprehensive Vertebrae Tumor Diagnosis. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_67
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