A hierarchical multi-scale neural network framework for schistocyte detection in blood smears

Peripheral blood smear morphological analysis remains fundamental in hematological disease diagnosis. However, existing publicly available medical datasets lack annotations for schistocytes—pivotal biomarkers integral for disease diagnosis, severity stratification, and monitoring therapeutic responsiveness. To address this gap, we constructed the first standardized blood smear dataset containing six cell types: erythrocytes, platelets, granulocytes, lymphocytes, monocytes, and schistocytes. This dataset comprises 6,150 high-resolution, pathologist-validated images with data augmentation, establishing a benchmark for complex morphological cell detection. To overcome multi-scale detection challenges (ranging from 2 to 50 μm), we propose a hierarchical multi-scale neural network framework (MCS-Net) integrating three innovations: (1) A multi-scale convolution and attention fusion module to extract local details and global semantic features; (2) A Cross-Scale Dynamic interaction detection head to mitigate high-level feature suppression of micro-targets while maintaining adequate receptive fields for large targets, enabling adaptive feature fusion in dense cell clusters; (3) A single-head self-attention mechanism to optimize leukocyte nuclear-cytoplasmic correlations, complemented by normalized Wasserstein distance for robust micro-target localization. Experiments demonstrate our framework achieves a mean average precision at 0.5 intersection-over-union (mean Average Precision (mAP)@0.5) of 0.958 ± 0.0022 on our internal dataset, surpassing the baseline by 3.9 ± 0.39%, with schistocyte detection precision reaching 90.1 ± 0.39%. The lightweight combination of the Cross-Scale Dynamic interaction detection head and normalized Wasserstein distance maintains a mAP@0.5 of 0.946 at 102 frames per second, providing an efficient solution for real-time medical imaging analysis. This work advances early screening tools for critical conditions such as Disseminated Intravascular Coagulation and Microangiopathic Hemolytic Anemia (DIC/MAHA) and drives algorithmic innovation in blood cell subtype detection.