Kanakasabapathy et al., 2021 Image Dataset
Article Title: Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images
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Description
This is a thin, Giemsa-stained blood smear image dataset
Demography
- Country: United States of America
- Study Location: Massachusetts General Hospital (MGH) IVF laboratory and the MGH clinical pathology laboratory
- Parasite’s species: Plasmodium falciparum
- Number of Patients: 8 patients
Imaging Technique
- Optical Train: Microscope
- Microscope Type: Benchtop microscope, a portable stand-alone 3D-printed microscope and a smartphone-based microscope
- Number of Images:
| Microscope | Number of Images |
|---|---|
| Benchtop microscope | 12635 |
| Portable Stand-alone 3D-printed Microscope | 11397 |
| Smartphone-based Microscope | 14469 |
Dataset Availability
| Status | Dataset Link | DOI |
|---|---|---|
| Publicly Available | Dataset | 10.1038/s41551-021-00733-w |
Cite this Article
❗🛑 If you are using this resource, please cite:
Kanakasabapathy, M. K., Thirumalaraju, P., Kandula, H., Doshi, F., Sivakumar, A. D., Kartik, D., Gupta, R., Pooniwala, R., Branda, J. A., Tsibris, A. M., Kuritzkes, D. R., Petrozza, J. C., Bormann, C. L., & Shafiee, H. (2021). Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images. Nature Biomedical Engineering, 5(6), 571–585. https://doi.org/10.1038/s41551-021-00733-w