Genetically Aligned Patient Representations Improve Hematological Diagnosis

• URL: http://arxiv.org/abs/2605.29980v1
• Date: Thu, 28 May 2026 14:17:31 GMT
• Title: Genetically Aligned Patient Representations Improve Hematological Diagnosis
• Abstract summary: Hematological pathology is unique in that visual single-cell evaluation is often paired with cytogenetics and molecular genetics for blood cancer diagnosis.<n>We present a framework to align single white blood cell images with chromosomal aberrations (karyotype) and somatic mutations from targeted gene panels.
• Score: 0.9453575851198832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal alignment of histopathology encoders with transcriptomic and genomic data has been shown to significantly improve performance in downstream diagnostic tasks. Hematological cytology is unique in that visual single-cell evaluation is often paired with cytogenetics and molecular genetics for blood cancer diagnosis. In this study, we present a framework to align single white blood cell images with chromosomal aberrations (karyotype) and somatic mutations from targeted gene panels. Our training strategy follows a two-stage approach: (i) self-supervised, vision-only pretraining of a transformer aggregator using an iBOT head on a cohort of over 1500 patients, and (ii) genetic alignment via supervised contrastive loss on acute myeloid leukemia patients. Our genetically aligned patient encoder improves hematological diagnostic tasks, outperforming slide-level histopathology foundation models. Additionally, the model provides off-the-shelf retrieval capabilities for diseases and genetic alterations. Incorporating genetic data into patient encoders increases the quality of patient representations, providing a framework that aligns with clinical diagnostic workflows and paves the way for future multimodal hematology-specific AI. The code and model weights are available at https://github.com/marrlab/GenBloom.

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