cAItomorph applies a transformer aggregator on DinoBloom embeddings to predict eight coarse hematological malignancy classes from peripheral blood single-cell images, achieving 0.72 accuracy and reducing false discovery rate for acute leukemia referrals.
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Transformer-Based Hematological Malignancy Prediction from Peripheral Blood Smears in a Real-World Cohort
cAItomorph applies a transformer aggregator on DinoBloom embeddings to predict eight coarse hematological malignancy classes from peripheral blood single-cell images, achieving 0.72 accuracy and reducing false discovery rate for acute leukemia referrals.