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Part 1: Document Description
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Citation |
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Title: |
Eosinophilic Esophagitis E-135_4 |
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Identification Number: |
doi:10.18130/V3/PS6BFH |
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Distributor: |
University of Virginia Dataverse |
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Date of Distribution: |
2024-11-12 |
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Version: |
1 |
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Bibliographic Citation: |
Sengupta, Saurav, 2024, "Eosinophilic Esophagitis E-135_4", https://doi.org/10.18130/V3/PS6BFH, University of Virginia Dataverse, V1 |
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Citation |
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Title: |
Eosinophilic Esophagitis E-135_4 |
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Identification Number: |
doi:10.18130/V3/PS6BFH |
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Identification Number: |
https://portal.ithriv.org/#/public_commons/project/4e5a27e3-3fdd-4e59-b54e-f4105ff1cc28/dataset/812aaac6-44d4-4538-9115-61b4e510cb7d |
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Authoring Entity: |
Sengupta, Saurav |
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Date of Production: |
2024-11-12 |
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Distributor: |
University of Virginia Dataverse |
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Access Authority: |
Sengupta, Saurav |
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Depositor: |
Sengupta, Saurav |
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Date of Deposit: |
2024-11-12 |
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Holdings Information: |
https://doi.org/10.18130/V3/PS6BFH |
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Study Scope |
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Keywords: |
Other, imaging, eoe, eosinophilic gastrointestinal disease, healthcare deep learning, gut intelligenceai |
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Abstract: |
Eosinophilic esophagitis (EoE) is an inflammatory disease diagnosed by ≥15 intraepithelial eosinophils (Eos) in an esophageal biopsy high-power field. This is a Whole Slide Image (WSI) containing eosinophils that we use to train an AI model to automatically detect and count these cellular structures in a given image to reduce the data annotation burden on clinicians and pathologists. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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