Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3213-3216. doi: 10.1109/EMBC46164.2021.9630536.
The wide spread of coronavirus pneumonia (COVID-19) has been a severe threat to global health since 2019. Apart from the nucleic acid detection, medical imaging examination is a vital diagnostic modality to confirm and treat the disease. Thus, implementing the automatic diagnosis of the COVID-19 bears particular significance. However, the limitations of data quality and size strongly hinder the clas-sification and segmentation performance and it also result in high misdiagnosis rate. To this end, we propose a novel full scale attention mechanism (FUSA) to capture more contextual dependencies of features, which enables the model easier to classify positive cases and improve the sensitivity. Specifically, FUSA parallelly extracts the information of channel domain and spatial domain, and fuses them together. The experimental study shows FUSA can significantly improve the COVID-19 automated diagnosis performance and eliminate false negative cases compared with other state-of-the-art ones.
自 2019 年以来，冠状病毒肺炎（COVID-19）的广泛传播对全球健康构成了严重威胁。除了核酸检测外，医学影像检查也是确认和治疗疾病的重要诊断方式。因此，实现 COVID-19 的自动诊断具有特殊意义。然而，数据质量和规模的限制严重阻碍了分类和分割性能，也导致了高误诊率。为此，我们提出了一种新颖的全尺度注意力机制（FUSA）来捕获特征的更多上下文依赖关系，从而使模型更容易对阳性病例进行分类，并提高了敏感性。具体来说，FUSA 并行地提取通道域和空间域的信息，并将它们融合在一起。实验研究表明，与其他最先进的方法相比，FUSA 可以显著提高 COVID-19 的自动诊断性能并消除假阴性病例。
