Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China.
Landing Artificial Intelligence Center for Pathological Diagnosis, Wuhan, 430072, China.
BMC Bioinformatics. 2022 Jul 15;23(1):282. doi: 10.1186/s12859-022-04824-6.
Via counting the different kinds of white blood cells (WBCs), a good quantitative description of a person's health status is obtained, thus forming the critical aspects for the early treatment of several diseases. Thereby, correct classification of WBCs is crucial. Unfortunately, the manual microscopic evaluation is complicated, time-consuming, and subjective, so its statistical reliability becomes limited. Hence, the automatic and accurate identification of WBCs is of great benefit. However, the similarity between WBC samples and the imbalance and insufficiency of samples in the field of medical computer vision bring challenges to intelligent and accurate classification of WBCs. To tackle these challenges, this study proposes a deep learning framework by coupling the pre-trained ResNet and DenseNet with SCAM (spatial and channel attention module) for accurately classifying WBCs.
In the proposed network, ResNet and DenseNet enables information reusage and new information exploration, respectively, which are both important and compatible for learning good representations. Meanwhile, the SCAM module sequentially infers attention maps from two separate dimensions of space and channel to emphasize important information or suppress unnecessary information, further enhancing the representation power of our model for WBCs to overcome the limitation of sample similarity. Moreover, the data augmentation and transfer learning techniques are used to handle the data of imbalance and insufficiency. In addition, the mixup approach is adopted for modeling the vicinity relation across training samples of different categories to increase the generalizability of the model. By comparing with five representative networks on our developed LDWBC dataset and the publicly available LISC, BCCD, and Raabin WBC datasets, our model achieves the best overall performance. We also implement the occlusion testing by the gradient-weighted class activation mapping (Grad-CAM) algorithm to improve the interpretability of our model.
The proposed method has great potential for application in intelligent and accurate classification of WBCs.
通过计算不同种类的白细胞（WBC），可以对一个人的健康状况进行很好的定量描述，从而形成对几种疾病进行早期治疗的关键方面。因此，正确分类 WBC 至关重要。不幸的是，手动显微镜评估既复杂又耗时，且具有主观性，因此其统计可靠性受到限制。因此，WBC 的自动和准确识别具有重要意义。然而，WBC 样本之间的相似性以及医学计算机视觉领域样本的不平衡和不足给 WBC 的智能和准确分类带来了挑战。为了应对这些挑战，本研究提出了一种深度学习框架，该框架通过将预先训练的 ResNet 和 DenseNet 与 SCAM（空间和通道注意力模块）耦合，用于准确分类 WBC。
在所提出的网络中，ResNet 和 DenseNet 分别实现了信息重用和新信息探索，这两者对于学习良好的表示都很重要且兼容。同时，SCAM 模块依次从空间和通道两个独立维度推断注意力图，以强调重要信息或抑制不必要信息，从而进一步增强模型对 WBC 的表示能力，克服样本相似性的限制。此外，还使用数据增强和迁移学习技术来处理不平衡和不足的数据。此外，采用混合方法对不同类别训练样本之间的邻近关系进行建模，以提高模型的泛化能力。通过与我们在开发的 LDWBC 数据集和公开可用的 LISC、BCCD 和 Raabin WBC 数据集上的五个代表性网络进行比较，我们的模型实现了最佳的整体性能。我们还通过梯度加权类激活映射（Grad-CAM）算法实现了遮挡测试，以提高模型的可解释性。
该方法在 WBC 的智能和准确分类方面具有很大的应用潜力。
