Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610041, China.
Int J Mol Sci. 2022 Nov 1;23(21):13355. doi: 10.3390/ijms232113355.
Blood cell detection is an essential branch of microscopic imaging for disease diagnosis. TE-YOLOF is an effective model for blood cell detection, and was recently found to have an outstanding trade-off between accuracy and model complexity. However, there is a lack of understanding of whether the dilated encoder in TE-YOLOF works well for blood cell detection. To address this issue, we perform a thorough experimental analysis and find the interesting fact that the dilated encoder is not necessary for TE-YOLOF to perform the blood cell detection task. For the purpose of increasing performance on blood cell detection, in this research, we use the attention mechanism to dominate the dilated encoder place in TE-YOLOF and find that the attention mechanism is effective to address this problem. Based upon these findings, we propose a novel approach, named Enhanced Channel Attention Module (ECAM), based on attention mechanism to achieve precision improvement with less growth on model complexity. Furthermore, we examine the proposed ECAM method compared with other tip-top attention mechanisms and find that the proposed attention method is more effective on blood cell detection task. We incorporate the spatial attention mechanism in CBAM with our ECAM to form a new module, which is named Enhanced-CBAM. We propose a new network named Enhanced Channel Attention Network (ENCANet) based upon Enhanced-CBAM to perform blood cell detection on BCCD dataset. This network can increase the accuracy to 90.3 AP while the parameter is only 6.5 M. Our ENCANet is also effective for conducting cross-domain blood cell detection experiments.
血细胞检测是疾病诊断中显微镜成像的一个重要分支。TE-YOLOF 是一种用于血细胞检测的有效模型，最近发现它在准确性和模型复杂度之间具有出色的折衷。然而，人们对于 TE-YOLOF 中的扩张编码器是否能很好地用于血细胞检测还缺乏了解。为了解决这个问题，我们进行了全面的实验分析，发现了一个有趣的事实，即扩张编码器对于 TE-YOLOF 执行血细胞检测任务并不是必需的。为了提高在血细胞检测上的性能，在这项研究中，我们使用注意力机制来主导 TE-YOLOF 中的扩张编码器位置，并发现注意力机制在解决这个问题上是有效的。基于这些发现，我们提出了一种新的方法，名为增强型通道注意力模块（ECAM），基于注意力机制，在模型复杂度增长较少的情况下提高精度。此外，我们将提出的 ECAM 方法与其他顶尖的注意力机制进行了比较，发现提出的注意力方法在血细胞检测任务上更有效。我们将空间注意力机制与我们的 ECAM 结合，形成了一个新的模块，命名为增强型 CBAM（eCBAM）。我们提出了一种新的网络，名为增强型通道注意力网络（ENCANet），基于增强型 CBAM 来对 BCCD 数据集进行血细胞检测。该网络可以在参数仅为 6.5M 的情况下将准确率提高到 90.3AP。我们的 ENCANet 对于进行跨域血细胞检测实验也很有效。
