DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
Paper • 2404.05022 • Published • 3
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: IndexError
Message: tuple index out of range
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 80, in _generate_tables
num_rows = _check_dataset_lengths(h5, self.info.features)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 358, in _check_dataset_lengths
if dset.shape[0] != num_rows:
~~~~~~~~~~^^^
IndexError: tuple index out of rangeNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
DinoBloom Hemato Patient Embeddings
Per-patient image embeddings of peripheral blood smears, extracted with the DinoBloom-B foundation model (code, paper).
Each .h5 file contains the stacked DinoBloom-B embeddings for all single-cell crops of one patient.
Contents
patient_embeddings/
├── caitomorph/ # 409 patients — caitomorph cohort (Dasdelen et al., 2026)
├── aml_hehr/ # 189 patients — AML genetic-subtype cohort (Hehr et al., 2023)
└── apl_aml/ # 106 patients — APL vs. AML cohort (Sidhom et al., 2021)
File layout
Each patient .h5 holds:
| key | shape | dtype | description |
|---|---|---|---|
features |
(N, 768) |
float32 | DinoBloom-B embedding per single-cell crop |
labels |
() |
int64 | patient-level class label |
Usage
import h5py
with h5py.File("patient_embeddings/caitomorph/ALK_183.h5", "r") as f:
features = f["features"][:] # (N_cells, 768)
label = int(f["labels"][()]) # patient label
Citation
If you use these embeddings, please cite DinoBloom (the embedding model) and the caitomorph paper (this embedding release):
@inproceedings{koch2024dinobloom,
title={DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology},
author={Koch, Valentin and Wagner, Sophia J. and Kazeminia, Salome and Sancar, Ece and Hehr, Matthias and Schnabel, Julia A. and Peng, Tingying and Marr, Carsten},
booktitle={MICCAI},
year={2024}
}
@article{dasdelen2026ai,
title={AI-based hematological malignancy prediction from peripheral blood smears in a large diagnostic laboratory cohort},
author={Dasdelen, Muhammed Furkan and Kukuljan, Ivan and Lienemann, Peter and Ozlugedik, Fatih and Sadafi, Ario and Hehr, Matthias and Spiekermann, Karsten and Pohlkamp, Christian and Marr, Carsten},
journal={Leukemia},
pages={1--5},
year={2026},
publisher={Nature Publishing Group UK London}
}
If you use the corresponding patient cohorts, please also cite the original dataset paper:
caitomorph/→ Dasdelen et al., 2026:
@article{dasdelen2026ai,
title={AI-based hematological malignancy prediction from peripheral blood smears in a large diagnostic laboratory cohort},
author={Dasdelen, Muhammed Furkan and Kukuljan, Ivan and Lienemann, Peter and Ozlugedik, Fatih and Sadafi, Ario and Hehr, Matthias and Spiekermann, Karsten and Pohlkamp, Christian and Marr, Carsten},
journal={Leukemia},
pages={1--5},
year={2026},
publisher={Nature Publishing Group UK London}
}
aml_hehr/→ Hehr et al., 2023:
@article{hehr2023explainable,
title={Explainable AI identifies diagnostic cells of genetic AML subtypes},
author={Hehr, Matthias and Sadafi, Ario and Matek, Christian and Lienemann, Peter and Pohlkamp, Christian and Haferlach, Torsten and Spiekermann, Karsten and Marr, Carsten},
journal={PLOS Digital Health},
volume={2},
number={3},
pages={e0000187},
year={2023},
publisher={Public Library of Science}
}
apl_aml/→ Sidhom et al., 2021:
@article{sidhom2021deep,
title={Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features},
author={Sidhom, John-William and Siddarthan, Ingharan J. and Lai, Bo-Shiun and Luo, Adam and Hambley, Bryan C. and Bynum, Jennifer and Duffield, Amy S. and Streiff, Michael B. and Moliterno, Alison R. and Imus, Philip and others},
journal={NPJ Precision Oncology},
volume={5},
number={1},
pages={38},
year={2021},
publisher={Nature Publishing Group}
}
- Downloads last month
- 29