HAPPY: a deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images Article Swipe
Claudia Vanea
,
Jelisaveta Džigurski
,
Valentina Rukins
,
Omri Dodi
,
Siim Siigur
,
Liis Salumäe
,
Karen Meir
,
W. Tony Parks
,
Drorith Hochner‐Celnikier
,
Abigail Fraser
,
Hagit Hochner
,
Triin Laisk
,
Linda M. Ernst
,
Cecilia M. Lindgren
,
Christoffer Nellåker
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.10535021
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.10535021
These two zipped folders contain all data necessary to train, validate and reproduce results from the paper. Unzipping the files will create 6 folders. Data from folders with the same name across both zips should be combined into one folder. The 'annotations' folder contains all ground truth annotations for training all three deep learning models. The 'datasets' folder contains images for training the nuclei localisation and cell classification models. The 'embeddings' folder contains cell embedding vectors and nuclei coordinates from two slides used to create nodes to train the graph tissue classification model. The 'graph_splits' folder contains regions defining the validation and test splits for the graph model. The 'slides' folder contains a sample region of a whole slide image as a .tiff file for running the inference demo. The 'trained_models' folder contains trained weights for each of the three models. Further instructions for dataset use and creation of custom datasets are available in the GitHub readme: https://github.com/Nellaker-group/happy.
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Metadata
- Type
- dataset
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.10535021
- OA Status
- green
- Cited By
- 1
- Related Works
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- OpenAlex ID
- https://openalex.org/W4393843868
All OpenAlex metadata
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https://openalex.org/W4393843868Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.10535021Digital Object Identifier
- Title
-
HAPPY: a deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide imagesWork title
- Type
-
datasetOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-19Full publication date if available
- Authors
-
Claudia Vanea, Jelisaveta Džigurski, Valentina Rukins, Omri Dodi, Siim Siigur, Liis Salumäe, Karen Meir, W. Tony Parks, Drorith Hochner‐Celnikier, Abigail Fraser, Hagit Hochner, Triin Laisk, Linda M. Ernst, Cecilia M. Lindgren, Christoffer NellåkerList of authors in order
- Landing page
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https://doi.org/10.5281/zenodo.10535021Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.10535021Direct OA link when available
- Concepts
-
Pipeline (software), Histology, Placenta, Computer science, Artificial intelligence, Biology, Pathology, Medicine, Pregnancy, Fetus, Programming language, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
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-
10Other works algorithmically related by OpenAlex
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