Abstract
Lesion detection is an essential technique in medical diagnostic systems. Since there are great differences in intensity and appearance within a same lesion category, lesion detection from computed tomography (CT) scans is still a challenging task. Sufficiently using 3D context information become the research hotpot in lesion detection area, since algorithms can benefit from geometry and texture of lesions. Motivated by this trend, we propose a multi-scale CNN based on 3D context fusion, called M3DCF, for extracting lesion area from CT scans. In order to speed up the algorithm, the one-stage regression-based detector, rather than region proposal network, is adopted. Specifically, we employ 3D context fusion strategy that allows M3DCF fusing features from neighboring slices. Finally, we use a multi-scale scheme to combine low-level and high-level features. This strategy allows us to get more meaningful semantic information. The experimental results conducted on DeepLesion dataset indicates that the proposed method outperformed state-of-the-arts, including RetinaNet, Faster R-CNN, and 3DCE. The source code is available on https://github.com/JMUAIA/M3DCF.
The first author is a student. This work is supported by the National Natural Science Foundation of China under Grant No. 61702251, the Key Technical Project of Fujian Province under Grant No. 2017H6015.
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Wu, Z., Chen, J., Wang, Z., Su, J., Cai, G. (2019). Multi-scale Convolutional Neural Network Based on 3D Context Fusion for Lesion Detection. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_49
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