Digital pathology has recently been revolutionized by advancements in
artificial intelligence, deep learning, and high-performance computing. With
its advanced tools, digital pathology can help improve and speed up the
diagnostic process, reduce human errors, and streamline the reporting step. In
this paper, we report a new large red blood cell (RBC) image dataset and
propose a two-stage deep learning framework for RBC image segmentation and
classification. The dataset is a highly diverse dataset of more than 100K RBCs
containing eight different classes. The dataset, which is considerably larger
than any publicly available hematopathology dataset, was labeled independently
by two hematopathologists who also manually created masks for RBC cell
segmentation. Subsequently, in the proposed framework, first, a U-Net model was
trained to achieve automatic RBC image segmentation. Second, an EfficientNetB0
model was trained to classify RBC images into one of the eight classes using a
transfer learning approach with a 5X2 cross-validation scheme. An IoU of 98.03%
and an average classification accuracy of 96.5% were attained on the test set.
Moreover, we have performed experimental comparisons against several prominent
CNN models. These comparisons show the superiority of the proposed model with a
good balance between performance and computational cost.

数字病理学发展迅速，结合人工智能、深度学习和高性能计算等技术的进步，可以提高和加速诊断过程，减少人为错误，并简化报告步骤。本文提出了一个新的大型红细胞（RBC）图像数据集，并提出了一个两阶段的深度学习框架用于 RBC 图像分割和分类，采用 U-Net 模型进行自动 RBC 图像分割，并使用 EfficientNetB0 模型进行 RBC 图像分类，取得了 98.03％的 IoU 和 96.5％的平均分类准确率。与其他知名 CNN 模型进行的实验比较表明，该模型在性能和计算成本之间取得了良好的平衡。