A shape and texture feature blending network for bone marrow cells classification using auxiliary learning
ZhengYang Guo, ZengBiao Yang, Xuanyu Xiang, Yihua Tan · 2024
Morphological examination of bone marrow cells is crucial for diagnosing blood diseases. However, manual classification of bone marrow cells is time-consuming and subjective. Therefore, it is necessary to develop an autoclassification method for bone marrow cells. Although deep learning methods are commonly used for cell classification like Resnet50, they don’t take advantage of the features of bone marrow cells such as the shape features of cells. However, the shape of cell and nucleus plays a significant role in distinguishing between different cell types. In this paper, we proposed a Shape and Texture Feature Blending Network (STFB-Net) for bone marrow cells classification based on auxiliary learning. We used ResNext50 as the backbone network for STFB-Net due to its exceptional ability to extract texture features from cells. In addition, we proposed the Shape Feature Extraction Module (SFEM) to enhance the backbone network's ability for capturing shape features. SFEM shares a part of parameters with the backbone network. SFEM extracts features at multiple scales and up-samples them, then fuses multi-scale features to predict the shape of cells. We performed experiments on two bone marrow cell datasets. The results show that the proposed STFB-Net can effectively extract texture and shape features, which brings better performance than other cell classification methods. By using the Grad-CAM method to visualize the features extracted by STFB-Net, we proved the reliability and the effectiveness of the STFB-Net in extracting cell shape features.