RC R-CNN for Bone Marrow Cell Recognition

The morphological examination of bone marrow (BM) cells is an essential basis for the diagnosis and assessment of hematologic diseases. However, manual recognition of cell class and quantification in microscopic images of BM cells is time-consuming, labor-intensive, and prone to errors. Therefore, it is crucial to study an efficient algorithm for the automatic recognition of BM cells. Clinically, to determine the BM cell classes, experienced examiners usually rely on surrounding cells as references to provide auxiliary confirmation, but there is no model specially designed for contextual relationships of BM cells. To extract cell features comprehensively and explore the contextual relationships among BM cells, we propose RC RCNN based on Mask R-CNN, which includes positional encoding (PE), ROI self-attention module (RSAM), and ROI channel attention module (RCAM) as an extension to Mask RCNN. The PE and RCAM enable better capturing of contextual information between BM cells. By using the RSAM, the model directs its attention toward internal cell features while effectively suppressing irrelevant background information. Experimental results demonstrate the outstanding performance of the proposed method, with the AP and F1-Score reaching 73.21% and 87.71%, respectively, an improvement of about 2.3% and 2.2% over the baseline.