Hu Bin, Liu Yang, Chu Pengzhi, Tong Minglei, Kong Qingjie
Department of Compute Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Department of Dermatology, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Physiol. 2022 Jun 17;13:911297. doi: 10.3389/fphys.2022.911297. eCollection 2022.
Object detection technology has been widely used in medical field, such as detecting the images of blood cell to count the changes and distribution for assisting the diagnosis of diseases. However, detecting small objects is one of the most challenging and important problems especially in medical scenarios. Most of the objects in medical images are very small but influential. Improving the detection performance of small objects is a very meaningful topic for medical detection. Current researches mainly focus on the extraction of small object features and data augmentation for small object samples, all of these researches focus on extracting the feature space of small objects better. However, in the training process of a detection model, objects of different sizes are mixed together, which may interfere with each other and affect the performance of small object detection. In this paper, we propose a method called pixel level balancing (PLB), which takes into account the number of pixels contained in the detection box as an impact factor to characterize the size of the inspected objects, and uses this as an impact factor. The training loss of each object of different size is adjusted by a weight dynamically, so as to improve the accuracy of small object detection. Finally, through experiments, we demonstrate that the size of objects in object detection interfere with each other. So that we can improve the accuracy of small object detection through PLB operation. This method can perform well with blood cell detection in our experiments.
目标检测技术已在医学领域得到广泛应用，例如检测血细胞图像以计数其变化和分布情况，辅助疾病诊断。然而，检测小目标尤其是在医学场景中是最具挑战性和重要的问题之一。医学图像中的大多数目标都非常小但却很有影响。提高小目标的检测性能对于医学检测来说是一个非常有意义的课题。当前的研究主要集中在小目标特征提取和小目标样本的数据增强上，所有这些研究都致力于更好地提取小目标的特征空间。然而，在检测模型的训练过程中，不同大小的目标混合在一起，可能会相互干扰，影响小目标检测的性能。在本文中，我们提出了一种称为像素级平衡（PLB）的方法，该方法将检测框中包含的像素数量作为一个影响因素来表征被检测目标的大小，并将其用作一个影响因素。通过权重动态调整不同大小的每个目标的训练损失，从而提高小目标检测的准确率。最后，通过实验，我们证明了目标检测中目标大小会相互干扰。这样我们可以通过PLB操作提高小目标检测的准确率。在我们的实验中，这种方法在血细胞检测方面表现良好。
