Summary of Dinobloom: a Foundation Model For Generalizable Cell Embeddings in Hematology, by Valentin Koch et al.
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
by Valentin Koch, Sophia J. Wagner, Salome Kazeminia, Ece Sancar, Matthias Hehr, Julia Schnabel, Tingying Peng, Carsten Marr
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel foundation model, DinoBloom, is introduced for single cell images in hematology. This AI-powered tool utilizes a tailored pipeline and draws upon an extensive dataset of over 380,000 white blood cell images from peripheral blood and bone marrow smears. The model outperforms existing medical and non-medical vision models in tasks such as cell-type classification and acute myeloid leukemia subtyping. A family of four DinoBloom models can be adapted for various downstream applications, serving as a strong baseline for classification problems and facilitating the assessment of batch effects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DinoBloom is a new way to use computers to help doctors diagnose diseases from blood cell images. Doctors have been trying to use AI for this purpose, but it was hard because there wasn’t enough information about what normal cells look like. This problem made it difficult for the computer to learn and get good at diagnosing. The DinoBloom team created a big collection of images and used them to train a special kind of AI model that can recognize different types of blood cells. They tested their model on some new, unseen pictures and found that it was much better than other models at doing this task. |
Keywords
* Artificial intelligence * Classification
