# DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology

arXiv: https://arxiv.org/abs/2404.05022 · HTML: https://mlsanity.com/paper/2404.05022/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Virchow: A Million-Slide Digital Pathology Foundation Model](https://mlsanity.com/paper/2309.07778/) (2023)
- [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020)
- [A General-Purpose Self-Supervised Model for Computational Pathology](https://mlsanity.com/paper/2308.15474/) (2023)
- [Low-resource finetuning of foundation models beats state-of-the-art in histopathology](https://mlsanity.com/paper/2401.04720/) (2024)
- [Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset](https://mlsanity.com/paper/2012.01321/) (2020)

## Built on by (most cited first, 27 of 27)

- [CytoSAE: Interpretable Cell Embeddings for Hematology](https://mlsanity.com/paper/2507.12464/) (2025)
- [Domain-incremental White Blood Cell Classification with Privacy-aware Continual Learning](https://mlsanity.com/paper/2503.19819/) (2025)
- [Quick ViTs: Speeding up Vision Transformers through Equivariance](https://mlsanity.com/paper/2505.15441/) (2025)
- [DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining](https://mlsanity.com/paper/2509.06990/) (2025)
- [OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis](https://mlsanity.com/paper/2602.04547/) (2026)
- [Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images](https://mlsanity.com/paper/2410.08926/) (2024)
- [RedDino: A foundation model for red blood cell analysis](https://mlsanity.com/paper/2508.08180/) (2025)
- [Uni-Hema: Unified Model for Digital Hematopathology](https://mlsanity.com/paper/2511.13889/) (2025)
- [CytoDINO: Risk-Aware and Biologically-Informed Adaptation of DINOv3 for Bone Marrow Cytomorphology](https://mlsanity.com/paper/2512.17930/) (2025)
- [Randomized-MLP Regularization Improves Domain Adaptation and Interpretability in DINOv2](https://mlsanity.com/paper/2511.05509/) (2025)
- [TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks](https://mlsanity.com/paper/2511.09605/) (2025)
- [Multi-Stage Fine-Tuning of Pathology Foundation Models with Head-Diverse Ensembling for White Blood Cell Classification](https://mlsanity.com/paper/2603.20383/) (2026)
- [Ensemble of Small Classifiers For Imbalanced White Blood Cell Classification](https://mlsanity.com/paper/2603.20856/) (2026)
- [A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts](https://mlsanity.com/paper/2604.23271/) (2026)
- [QG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging](https://mlsanity.com/paper/2606.20027/) (2026)
- [Towards Field-Ready AI-based Malaria Diagnosis: A Continual Learning Approach](https://mlsanity.com/paper/2507.23648/) (2025)
- [Equivariant Sparse Autoencoders: Mechanistic Interpretability of Neural Networks on Symmetric Data](https://mlsanity.com/paper/2511.09432/) (2025)
- [LEMON: a foundation model for nuclear morphology in Computational Pathology](https://mlsanity.com/paper/2603.25802/) (2026)
- [PBSBench: A Multi-Level Vision-Language Framework and Benchmark for Hematopathology Whole Slide Image Interpretation](https://mlsanity.com/paper/2604.17570/) (2026)
- [Vision Transformer-Conditioned UNet for Domain-Adaptive Semantic Segmentation](https://mlsanity.com/paper/2605.16393/) (2026)
- [Genetically Aligned Patient Representations Improve Hematological Diagnosis](https://mlsanity.com/paper/2605.29980/) (2026)
- [MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoring](https://mlsanity.com/paper/2606.22649/) (2026)
- [Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning](https://mlsanity.com/paper/2606.25770/) (2026)
- [Retrieval-Augmented Vision Foundation Models for Robust Leukemia Cell Classification across Multiple Microscopy Datasets](https://mlsanity.com/paper/2608.10657/) (2026)
- [Can You Trust Frozen Hematology Foundation Models under Acquisition Shift?](https://mlsanity.com/paper/2608.25148/) (2026)
- [HemaHier: Chain-Conditioned Ordinal Hierarchies for Lineage-Aware Bone-Marrow Cytology](https://mlsanity.com/paper/2609.12835/) (2026)
- [Refining Cytology Predictions with Conditional Random Fields](https://mlsanity.com/paper/2609.31028/) (2026)

## Abstract (arXiv)

In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in peripheral blood or bone marrow smears. However, clinical adoption of computational models has been hampered by the lack of generalization due to large batch effects, small dataset sizes, and poor performance in transfer learning from natural images. To address these challenges, we introduce DinoBloom, the first foundation model for single cell images in hematology, utilizing a tailored DINOv2 pipeline. Our model is built upon an extensive collection of 13 diverse, publicly available datasets of peripheral blood and bone marrow smears, the most substantial open-source cohort in hematology so far, comprising over 380,000 white blood cell images. To assess its generalization capability, we evaluate it on an external dataset with a challenging domain shift. We show that our model outperforms existing medical and non-medical vision models in (i) linear probing and k-nearest neighbor evaluations for cell-type classification on blood and bone marrow smears and (ii) weakly supervised multiple instance learning for acute myeloid leukemia subtyping by a large margin. A family of four DinoBloom models (small, base, large, and giant) can be adapted for a wide range of downstream applications, be a strong baseline for classification problems, and facilitate the assessment of batch effects in new datasets. All models are available at github.com/marrlab/DinoBloom.