Detecting cerebral microbleeds via deep learning with features enhancement by reusing ground truth. (June 2021) - Li, Tianfu; Zou, Yan; Bai, Pengfei; Li, Shixiao; Wang, Huawei; Chen, Xingliang; Meng, Zhanao; Kang, Zhuang; Zhou, Guofu
Highlights: We present a novel method of reusing ground-truth information to improve the performance of detecting cerebral microbleeds by using the SSD algorithm. The ground-truth information is using to stabilize the feature in forwarding propagation of the convolution network. Compared with baseline results, using the feature enhancement achieve more effective results. This method is flexible to be integrated into other object detecting methods. Abstract: Background and objectives: Cerebral microbleeds (CMBs) are cerebral small vascular diseases and are often used to diagnose symptoms such as stroke and dementia. Manual detection of cerebral microbleeds is a time-consuming and error-prone task, so the application of microbleed detection algorithms based on deep learning is of great significance. This study presents the feature enhancement technology applying to improve the performances of detecting CMBs. The primary purpose of the feature enhancement is emphasizing the meaningful features, leading deep learning network easier and correctly to optimize. Method: In this study, we applied feature enhancement in detecting CMBs from brain MRI images. Feature enhancement enhanced specific intervals and suppressed the useless intervals of the feature map. This method was applied in SSD-512 and SSD-300 algorithm, using VGG architecture pre-trained in the ImageNet dataset. Results: The proposed method was applied in SSD-512. Moreover, the model was trained and tested on the sequence of SWAN images of brain MRI images. The results of the experiment demonstrate that our method effectively improves the detection performance of the SSD network in detecting CMBs. We train SSD-512 120000 iterations and test results on the test datasets, by applying the feature enhancement layer, improving the precision with 3.3% and the mAP of 2.3%. In the same way, we trained SSD-300, improving the mAP of 2.0%. 2.8% and 7.4% precision are improved by applying feature enhancement layer In ResNet-34 and MobileNet. Conclusions: The proposed method achieved more effective performance, demonstrated that feature enhancement can be a helpful algorithm to enhance the deep learning model.