Abstract
Intracranial hematoma is a common and serious secondary lesion in craniocerebral injury, using computer aided diagnosis (CAD) system to assist clinicians to complete various tasks can greatly improve the accuracy and efficiency of hospital diagnosis. The traditional method require manual annotations of a large number of pathological feature data to train a voxel-level classifier. However, intracranial hematoma may occur in different parts of the brain and vary in severity. At the same time, there is great differences between the same subcategories and certain similarities between different subcategories of intracranial hematoma. Traditional methods require professionals to spend a lot of time to complete the part annotation and the annotation process is difficult. In order to reduce the cost of annotation and to get accurate results only by image level annotation, we propose a new intracranial hematoma classification network model (PHBP) based on the hierarchical bilinear pooling method. This model only uses the dataset of image level annotation to realize the cross-layer bilinear pooling communication of feature maps at different scales, and has better interpretability and sensitivity to the lesion area. In addition, to investigate the effectiveness of our approach, we establish a dataset of three types of hematoma (epidural hematoma, subdural hematoma and intracerebral hematoma) combined with physician’s diagnostic reports. In the experimental part, we compare our method with several state-of-the-art classification methods and the results show that our method has better performance.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (No.61876002, 62076005).
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Zhao, H., Wu, X., Bao, D., Zhang, S. (2021). Intracranial Hematoma Classification Based on the Pyramid Hierarchical Bilinear Pooling. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_51
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DOI: https://doi.org/10.1007/978-3-030-88010-1_51
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