Abstract：
Vaginitis is a common disease among women and has a high recurrence rate. The primary diagnosis method is fluorescence microscopic inspection, but manual inspection is inefficient and can lead to false detection or missed detection. Automatic cell identification and localization in microscopic images are necessary. For vaginitis diagnosis, clue cells and trichomonas are two important indicators and are difficult to be detected because of the different scales and image characteristics. This study proposes a Multi-Scale Perceptual YOLO (MSP-YOLO) with super-resolution reconstruction branch to meet the detection requirements of clue cells and trichomonas. Based on the scales and image characteristics of clue cells and trichomonas, we employed a super-resolution reconstruction branch to the detection network. This branch guides the detection branch to focus on subtle feature differences. Simultaneously, we proposed an attention-based feature fusion module that is injected with dilated convolutional group. This module makes the network pay attention to the non-centered features of the large target clue cells, which contributes to the enhancement of detection sensitivity. Experimental results show that the proposed detection network MSP-YOLO can improve sensitivity without compromising specificity. For clue cell and trichomoniasis detection, the proposed network achieved sensitivities of 0.706 and 0.910, respectively, which were 0.218 and 0.051 higher than those of the baseline model. In this study, the characteristics of the super-resolution reconstruction task are used to guide the network to effectively extract and process image features. The novel proposed network has an increased sensitivity, which makes it possible to detect vaginitis automatically.
摘要：
阴道炎是女性常见疾病，复发率高。主要诊断方法是荧光显微镜检查，但是手动检查效率低下，可能导致错误检测或漏检。需要在显微图像中自动识别和定位细胞。对于阴道炎的诊断，线索细胞和毛滴虫是两个重要的指标，由于尺度和图像特征的不同，很难被检测到。本研究提出了一种具有超分辨率重建分支的多尺度感知YOLO（MSP-YOLO），以满足线索细胞和滴虫的检测要求。根据线索细胞和毛滴虫的尺度和图像特征，我们在检测网络中采用了超分辨率重建分支。该分支引导检测分支专注于细微的特征差异。同时,我们提出了一个基于注意力的特征融合模块，该模块注入了扩张卷积组。该模块使网络关注大目标线索单元的非中心特征,这有助于提高检测灵敏度。实验结果表明，所提出的检测网络MSP-YOLO可以在不损害特异性的情况下提高灵敏度。对于线索细胞和毛滴虫的检测，拟议网络的灵敏度分别为0.706和0.910，比基线模型高0.218和0.051。在这项研究中,超分辨率重建任务的特征用于指导网络有效地提取和处理图像特征。新提出的网络具有更高的灵敏度，这使得自动检测阴道炎成为可能。
