Classification of hematologic malignancies from whole-slide bone marrow aspirates using a two-stage deep convolutional neural network pipeline.

Objectives: Morphologic information from bone marrow (BM) examinations remains critical for diagnosing leukemia and lymphoma, yet manual interpretation of BM smear slides is labor-intensive for hematologists and pathologists. Few automatic diagnostic tools have successfully classified whole slide images (WSIs) of hematologic malignancies. This study aimed to develop a deep convolutional neural network (DCNN) pipeline to classify leukemia subtypes and lymphoma using whole-slide BM aspirate images. Methods: The process involved two stages: first, a quality assessment model selected 200 regions of interest (ROIs) from each WSI to exclude non-informative areas. Next, eight DCNN models with different architectures were trained to classify each ROI into one of five hematologic malignancies, and tile-level predictions were aggregated to produce patient-level results. External evaluation and ancillary analyses were performed to demonstrate generalizability and robustness. Results: In total, 1,022 WSIs were enrolled. The results showed average patient-level accuracy, balanced accuracy, F1-score, and area under the receiver operating characteristic curve (AUC) of 95.2%, 94.7%, 94.8%, and 0.993, respectively, with DenseNet121 achieving the highest balanced accuracy (97.6%). Our method outperformed clustering-constrained attention multiple instance learning (CLAM) in comparison study (accuracy 97.1% vs. 85.0%) and reached accuracies of 83.8% and 86.7% on external datasets. Visualization maps showed consistency between model salience and cell distribution. Conclusion: Our results demonstrate the ability of DCNNs to achieve accurate diagnosis in hematologic pathology, and the pipeline holds potential to assist in early diagnosis and workflow augmentation for hematologists and pathologists.