Bone Marrow Cell Recognition: Training Deep Object Detection with A New Loss Function
This work addresses the need for objective quantitative analysis in bone marrow cell morphology, which is currently reliant on subjective doctor assessments, though it is incremental as it builds on existing YOLOv5 methods.
The paper tackled the problem of bone marrow cell detection for diagnosing blood diseases by proposing a YOLOv5-based algorithm with a novel loss function that accounts for class similarities, resulting in improved performance compared to other algorithms.
For a long time, bone marrow cell morphology examination has been an essential tool for diagnosing blood diseases. However, it is still mainly dependent on the subjective diagnosis of experienced doctors, and there is no objective quantitative standard. Therefore, it is crucial to study a robust bone marrow cell detection algorithm for a quantitative automatic analysis system. Currently, due to the dense distribution of cells in the bone marrow smear and the diverse cell classes, the detection of bone marrow cells is difficult. The existing bone marrow cell detection algorithms are still insufficient for the automatic analysis system of bone marrow smears. This paper proposes a bone marrow cell detection algorithm based on the YOLOv5 network, trained by minimizing a novel loss function. The classification method of bone marrow cell detection tasks is the basis of the proposed novel loss function. Since bone marrow cells are classified according to series and stages, part of the classes in adjacent stages are similar. The proposed novel loss function considers the similarity between bone marrow cell classes, increases the penalty for prediction errors between dissimilar classes, and reduces the penalty for prediction errors between similar classes. The results show that the proposed loss function effectively improves the algorithm's performance, and the proposed bone marrow cell detection algorithm has achieved better performance than other cell detection algorithms.