Qianqian Zhu

Xin Zhao, Qianqian Zhu, Jialing Wu

To address the challenges of similarity between lesions and surrounding tissues, overlapping appearances of partially benign and malignant nodules, and difficulty in classification, a deep learning network that integrates CNN and Transformer is proposed for the classification of benign and malignant breast lesions in ultrasound images. This network adopts a dual-branch architecture for local-global feature extraction, making full use of the advantages of CNN in extracting local features and the ability of ViT to extract global features to enhance the network's feature extraction capabilities for breast nodules. The local feature extraction branch employs a residual network with multiple attention-guided modules, which can effectively capture the local details and texture features of breast nodules, enhance sensitivity to subtle changes within the nodules, and thus can aid in accurate classification of their benign and malignancy. The global feature extraction branch utilizes the multi-head self-attention ViT network, which can capture the overall shape, boundary, and relationship with surrounding tissues, and thereby enhancing the understanding and modeling of both nodule and global image features. Experimental results on a public ultrasound breast nodule data set show that the proposed method is better than other comparison networks, This indicates that the fusion of CNN and Transformer networks can effectively improve the performance of the classification model and provide a powerful solution for the benign-malignant classification of ultrasound breast.

Wei Zheng, Guogang Zhang, Chenchen Zhao et al.

Power transformer plays a critical role in grid infrastructure, and its diagnosis is paramount for maintaining stable operation. However, the current methods for transformer diagnosis focus on discrete dissolved gas analysis, neglecting deep feature extraction of multichannel consecutive data. The unutilized sequential data contains the significant temporal information reflecting the transformer condition. In light of this, the structure of multichannel consecutive data cross-extraction (MCDC) is proposed in this article in order to comprehensively exploit the intrinsic characteristic and evaluate the states of transformer. Moreover, for the better accommodation in scenario of transformer diagnosis, one dimensional convolution neural network attention (1DCNN-attention) mechanism is introduced and offers a more efficient solution given the simplified spatial complexity. Finally, the effectiveness of MCDC and the superior generalization ability, compared with other algorithms, are validated in experiments conducted on a dataset collected from real operation cases of power transformer. Additionally, the better stability of 1DCNN-attention has also been certified.

Yujian Hu, Yilang Xiang, Yan-Jie Zhou et al.

The accurate and timely diagnosis of acute aortic syndromes (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic CT angiography (CTA) is the imaging protocol of choice in patients with suspected AAS. However, due to economic and workflow constraints in China, the majority of suspected patients initially undergo non-contrast CT as the initial imaging testing, and CTA is reserved for those at higher risk. In this work, we present an artificial intelligence-based warning system, iAorta, using non-contrast CT for AAS identification in China, which demonstrates remarkably high accuracy and provides clinicians with interpretable warnings. iAorta was evaluated through a comprehensive step-wise study. In the multi-center retrospective study (n = 20,750), iAorta achieved a mean area under the receiver operating curve (AUC) of 0.958 (95% CI 0.950-0.967). In the large-scale real-world study (n = 137,525), iAorta demonstrated consistently high performance across various non-contrast CT protocols, achieving a sensitivity of 0.913-0.942 and a specificity of 0.991-0.993. In the prospective comparative study (n = 13,846), iAorta demonstrated the capability to significantly shorten the time to correct diagnostic pathway. For the prospective pilot deployment that we conducted, iAorta correctly identified 21 out of 22 patients with AAS among 15,584 consecutive patients presenting with acute chest pain and under non-contrast CT protocol in the emergency department (ED) and enabled the average diagnostic time of these 21 AAS positive patients to be 102.1 (75-133) mins. Last, the iAorta can help avoid delayed or missed diagnosis of AAS in settings where non-contrast CT remains the unavoidable the initial or only imaging test in resource-constrained regions and in patients who cannot or did not receive intravenous contrast.