Abstract
Spinal dislocation diagnosis manifests typical characteristics of fine-grained visual categorization tasks, i.e. low inter-class variance and high intra-class variance. A pure data-driven approach towards an automated spinal dislocation diagnosis method would demand not only large volume of training data but also fine-grained labels, which is impractical in medial scenarios. In this paper, we attempt to utilize the expert knowledge that the spinal edges are crucial for dislocation diagnosis to guide model training and explore a data-knowledge dual driven approach for spinal dislocation diagnosis. Specifically, to embed the expert knowledge into the classification networks, we introduce a spatial regularization term to constrain the location of the discriminative regions of spinal CT images. Extensive experimental analysis has shown that the proposed method gains 0.18%–4.79% upon AUC, and the gain is more significant for smaller training sets. What’s more, the spatial regularization brings more discriminative and interpretable features.
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Lai, B. et al. (2019). Spatial Regularized Classification Network for Spinal Dislocation Diagnosis. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_2
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DOI: https://doi.org/10.1007/978-3-030-32692-0_2
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