---
license: apache-2.0
tags:
- vision
- image-classification
---

### (Ovarian) Ovarian Carcinoma

This model can additionally be run on our [pathology reports platform](https://www.pathologyreports.ai/marketplace/browse/14b2473e-17f4-4b68-b250-416f06fb20f5)

Credits: Dr. Noor Alsafwani (King Fahd Hospital of the University, Saudi Arabia)

### Introduction

This H&E ovarian carcinoma 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 ovarian serous carcinoma and other surrounding tissue elements.

Annotations were carried out on batches of image tiles (dimensions: 512 x 512 px) grouped using image-based clustering [(HAVOC, DOI: 10.1126/sciadv.adg1894)](https://doi.org/10.1126/sciadv.adg1894) from 8 publicly available TCGA-OV H&E-stained whole slide images. Validation testing was carried out on non-overlapping cases from TCGA.

### Classes
1. Blank Space
2. Fatty Tissue
3. Epithelial Tumor
4. Fibrous Tissue
5. Mixture Of Epithelial Tumor And Fibrous Tissue
6. Necrosis
7. Normal Ovarian Parenchyma
8. Tumor edge
9. Hemorrhage

This information can be found in the inference.json file

### Evaluation Metrics

Classifier validation can be found on the [pathology reports platform](https://www.pathologyreports.ai/marketplace/browse/14b2473e-17f4-4b68-b250-416f06fb20f5)