Interpretable multiple instance learning for hematologic diagnosis from peripheral blood smears
Siddharth Singi, Shenghuan Sun, Zhanghan Yin, Riya Gupta, Dylan C. Webb, Khawaja Hasan Bilal, Deepika Dilip, Linlin Wang, Neeraj Kumar, Swaraj Nanda, Nicholas Sanchez, Jacob Cleaves, Brenda Fried, Sean Paulsen, Ethan S. Yan, Ali Kamali, Argho Sarkar, Allyne Manzo, Jeeyeon Baik, İrem Şahver İşgör · Communications Medicine · 2026
Accurate diagnosis of hematologic malignancies from peripheral blood smears (PBSs) requires integrating cellular morphology and composition across hundreds of white blood cells. Existing approaches primarily automate single-cell classification and do not provide whole-slide diagnostic predictions. We present a full network that utilizes a highly performative cell-based encoder (DeepHeme) for feature extraction paired with our weakly supervised framework using attention-based multiple instance learning (MIL) that we call CAREMIL (Cell AggRegation, Explainable, Multiple Instance Learning). Upon evaluating various popular image encoders and MIL architectures, the combination of DeepHeme and CAREMIL is the best performing pipeline on our disease classification task. CAREMIL proves to be a robust aggregation function that outperforms the most commonly used slide level aggregation function (gated multiple instance learning) across several encoder types. The greatest improvements in performance gain with CAREMIL is observed when using out-of-domain encoders, including an encoder trained on ImageNet and leading open-source pathology foundational models (UNI2 and Virchow2). CAREMIL plus DeepHeme achieves the highest diagnostic performance across acute leukemia (AML), myelodysplastic syndromes (MDS), and hairy cell leukemia (HCL) (AUROCs 0.999, 0.891, and 0.945, respectively), and identifies AML disease even in cases with minimal or absent circulating blasts. Attention values assigned by CAREMIL highlight diagnostically relevant cells and reveal disease-specific morphometric signatures, enabling biological interpretability and case-level insight. CAREMIL remains robust to misclassified cell types by the cell image encoder and does not require explicit cell-level supervision. These findings position CAREMIL as an effective and interpretable multiple instance learning framework for hematologic slide diagnosis, with potential to extend to bone marrow aspirates, cytology, and other liquid biopsy specimens, and to support a broader shift toward quantitative, morphology-informed diagnostics in hematology.