  Fine-grained classification and counting of bone marrow erythroid cells are
vital for evaluating the health status and formulating therapeutic schedules
for leukemia or hematopathy. Due to the subtle visual differences between
different types of erythroid cells, it is challenging to apply existing
image-based deep learning models for fine-grained erythroid cell
classification. Moreover, there is no large open-source datasets on erythroid
cells to support the model training. In this paper, we introduce BMEC (Bone
Morrow Erythroid Cells), the first large fine-grained image dataset of
erythroid cells, to facilitate more deep learning research on erythroid cells.
BMEC contains 5,666 images of individual erythroid cells, each of which is
extracted from the bone marrow erythroid cell smears and professionally
annotated to one of the four types of erythroid cells. To distinguish the
erythroid cells, one key indicator is the cell shape which is closely related
to the cell growth and maturation. Therefore, we design a novel shape-aware
image classification network for fine-grained erythroid cell classification.
The shape feature is extracted from the shape mask image and aggregated to the
raw image feature with a shape attention module. With the shape-attended image
feature, our network achieved superior classification performance (81.12\%
top-1 accuracy) on the BMEC dataset comparing to the baseline methods. Ablation
studies also demonstrate the effectiveness of incorporating the shape
information for the fine-grained cell classification. To further verify the
generalizability of our method, we tested our network on two additional public
white blood cells (WBC) datasets and the results show our shape-aware method
can generally outperform recent state-of-the-art works on classifying the WBC.
The code and BMEC dataset can be found on https://github.com/wangye8899/BMEC.
