论文标题
细胞感染者：结合自我注意和卷积以进行细胞检测
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection
论文作者
论文摘要
显微镜图像中的细胞检测对于研究细胞如何移动和与环境相互作用很重要。最新的基于深度学习的细胞检测方法使用卷积神经网络（CNN）。但是，受到其他计算机视觉应用程序成功的启发，视觉变压器（VIT）也用于此目的。我们提出了一种新型混合CNN-VIT模型，用于显微镜图像中的细胞检测，以利用两种类型的深度学习模型的优势。我们采用有效的CNN，该CNN已在ImageNet数据集上进行了预先训练，以提取图像特征并利用转移学习来减少所需的训练数据的数量。提取的图像特征通过卷积和变压器层的结合进一步处理，以便卷积层可以专注于本地信息和变压器层上的全局信息。我们的基于质心细胞检测方法将细胞表示为椭圆，并且是端到端的训练。此外，我们表明我们提出的模型可以在四个不同的2D显微镜数据集上胜过完全卷积的单阶段探测器。代码可在以下网址找到：https：//github.com/roydenwa/cell-centroid-former
Cell detection in microscopy images is important to study how cells move and interact with their environment. Most recent deep learning-based methods for cell detection use convolutional neural networks (CNNs). However, inspired by the success in other computer vision applications, vision transformers (ViTs) are also used for this purpose. We propose a novel hybrid CNN-ViT model for cell detection in microscopy images to exploit the advantages of both types of deep learning models. We employ an efficient CNN, that was pre-trained on the ImageNet dataset, to extract image features and utilize transfer learning to reduce the amount of required training data. Extracted image features are further processed by a combination of convolutional and transformer layers, so that the convolutional layers can focus on local information and the transformer layers on global information. Our centroid-based cell detection method represents cells as ellipses and is end-to-end trainable. Furthermore, we show that our proposed model can outperform fully convolutional one-stage detectors on four different 2D microscopy datasets. Code is available at: https://github.com/roydenwa/cell-centroid-former
