Abstract
Accurate lesion detection in computer tomography (CT) slices benefits pathologic organ analysis in the medical diagnosis process. More recently, it has been tackled as an object detection problem using the Convolutional Neural Networks (CNNs). Despite the achievements from off-the-shelf CNN models, the current detection accuracy is limited by the inability of CNNs on lesions at vastly different scales. In this paper, we propose a Multi-Scale Booster (MSB) with channel and spatial attention integrated into the backbone Feature Pyramid Network (FPN). In each pyramid level, the proposed MSB captures fine-grained scale variations by using Hierarchically Dilated Convolutions (HDC). Meanwhile, the proposed channel and spatial attention modules increase the network’s capability of selecting relevant features response for lesion detection. Extensive experiments on the DeepLesion benchmark dataset demonstrate that the proposed method performs superiorly against state-of-the-art approaches.
Q. Shao and L. Gong contribute equally and share the first authorship. This work was done when Q. Shao was an intern in Tencent Youtu Lab. The source code and results are available at https://github.com/shaoqb/multi_scale_booster.
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Acknowledgments
This work was founded by the Key Area Research and Development Program of Guangdong Province, China (No. 2018B010111001).
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Shao, Q., Gong, L., Ma, K., Liu, H., Zheng, Y. (2019). Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-scale Booster. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_34
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DOI: https://doi.org/10.1007/978-3-030-32226-7_34
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