Background and objectives
Leukemia is one of the most common cancers in the UK and it is usually initially diagnosed through the time-consuming and subjective analysis of blood films by an expert hematologist. When a small number of blast cells may be present on a blood film, it is difficult to detect them even after a thorough review. Automating blood film image analysis could significantly speed up the process and improve diagnostic accuracy. This study benchmarks a machine learning framework based on vision transformers (ViTs) for automated blast detection in digitized blood films, evaluating their generalizability across public and clinical datasets.
Methods
We investigated different training strategies (hold-out/k-fold cross-validation), optimization (Adam or stochastic gradient descent (SGD)), and data preprocessing techniques (data augmentation, Gaussian pyramid downsampling) to assess their impact on the ViT performance when tested using both public (ALL-IDB) and clinical datasets from Barts Health NHS Trust.
Results
Models trained with Adam performed better than those trained with SGD. The best-performing model, ViT2-Adam, achieved the highest accuracy (≥0.86) and area under the receiver operating characteristic curvearea under the curve (AUROC ≥ 0.95), which exceeded other stochastic models demonstrating its potential for integration into clinical diagnostic workflows.
Conclusions
Our findings support the viability of ViTs for clinical integration in blood film analysis. Augmentation, advanced data splitting, and Gaussian downsampling enhance model generalization, offering a promising strategy for resource-limited or high-throughput diagnostic environments.
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