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license: apache-2.0 |
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tags: |
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- vision |
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- image-classification |
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### (Lung) Lung Adenocarcinoma |
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This model can additionally be run on our [pathology reports platform](https://www.pathologyreports.ai/marketplace/browse/40e62b8e-f41b-4ff1-90d8-03940c11068a) |
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Credits: Dr. Assem Alrumeh |
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### Introduction |
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This H&E lung adenocarcinoma tissue classifier was developed using transfer learning on a histology optimized version of the VGG19 CNN [(DOI: 10.1038/s42256-019-0068-6)](https://doi.org/10.1038/s42256-019-0068-6) and trained to recognize lung adenocarcinoma and other surrounding tissue elements. |
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Annotations were carried out on batches of image tiles (dimensions: 256 x 256 um) grouped using image-based clustering [(HAVOC, DOI: 10.1126/sciadv.adg1894)](https://doi.org/10.1126/sciadv.adg1894) from 10 publicly available TCGA-LUAD H&E-stained whole slide images. Validation testing was carried out on non-overlapping cases from TCGA. |
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### Classes |
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1. Adenocarcinoma |
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2. Blank space |
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3. Fibroconnective and stromal elements |
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4. Lung parenchyma |
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5. Lymphoid tissue |
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6. Necrosis |
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This information can be found in the inference.json file |
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### Evaluation Metrics |
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Classifier validation can be found on the [pathology reports platform](https://www.pathologyreports.ai/marketplace/browse/40e62b8e-f41b-4ff1-90d8-03940c11068a) |
