CytoDINO: Risk-Aware and Biologically-Informed Adaptation of DINOv3 for Bone Marrow Cytomorphology

2025-12-09

AI Summary

Morphological analysis of bone marrow cells is critical for diagnosing hematologic malignancies, yet manual interpretation is time-consuming, subjective, and entails asymmetric diagnostic risks—misclassifying blasts as normal cells poses significantly greater clinical harm than the reverse. To address this, we propose the first clinical risk-aware, lightweight pathological analysis framework. Our method introduces a Hierarchical Focal Loss (HFL) that incorporates lineage-specific biological priors to impose stronger penalties on high-risk misclassifications. It further integrates parameter-efficient fine-tuning via DINOv3-LoRA, confidence-driven selective prediction, and knowledge-guided modeling of inter-class relationships. Evaluated on the MLL dataset, our framework achieves 88.2% weighted F1 and 76.5% macro-F1 scores while training only 8% of parameters. It is deployable on a single RTX 5080 GPU, and its selective prediction mechanism covers 67% of samples with 99.5% accuracy.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Bone marrow cell cytomorphology analysis is critical for the diagnosis of hematological malignancies but remains a labor-intensive process subject to significant inter-observer variability. While recent foundation models have shown promise in computational pathology, they often require extensive computational resources and fail to account for the asymmetric risks associated with clinical misdiagnosis. We introduce CytoDINO, a framework that achieves state-of-the-art performance on the Munich Leukemia Laboratory (MLL) dataset by fine-tuning DINOv3 using Low-Rank Adaptation (LoRA). Our primary contribution is a novel Hierarchical Focal Loss with Critical Penalties, which encodes biological relationships between cell lineages and explicitly penalizes clinically dangerous misclassifications (e.g., classifying blasts as normal cells). CytoDINO achieves an 88.2% weighted F1 score and 76.5% macro F1 on a held-out test set of 21 cell classes. By utilizing parameter-efficient fine-tuning with only 8% trainable parameters on a single NVIDIA RTX 5080, we demonstrate that consumer-grade hardware can match specialized infrastructure. Furthermore, confidence-based selective prediction yields 99.5% accuracy on 67% of samples, suggesting a viable pathway for clinical deployment where high-uncertainty cases are flagged for expert review

Problem

• Automates bone marrow cell analysis to reduce labor and variability
• Addresses computational inefficiency and clinical risk in existing models
• Enables accurate cytomorphology classification on consumer-grade hardware

Innovation

• Fine-tunes DINOv3 using Low-Rank Adaptation (LoRA)
• Introduces Hierarchical Focal Loss with Critical Penalties
• Employs confidence-based selective prediction for deployment