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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+tags:
+- vision
+- image-classification
+---
+
+### (Testes) Testicular Cancer
+
+This model can additionally be run on our [pathology reports platform](https://www.pathologyreports.ai/marketplace/browse/bb5cb477-ad3c-464f-a75d-bbb4ae0d27c9)
+
+Credits: Dr. Nikfar Nikzad (McMaster University, Canada)
+
+### Introduction
+
+This H&E testicular cancer 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 testicular cancer 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 10 local H&E-stained whole slide images. Validation testing was carried out on non-overlapping image tiles from the same cases.
+
+
+### Classes
+
+1. Mature Epithelial Pattern
+2. Fibroconnective Tissue
+3. Mature Meningothelial Histology
+4. Mature Neural Tissue (Teratoma)
+5. Normal Hematolymphoid Tissue
+6. Rete Testis
+7. Seminiferous Tubules
+8. Seminoma
+9. Necrotic Tissue
+10. Skeletal Muscle Tissue
+11. Smooth Muscle Tissue
+12. Keratinous Material
+13. Yolk Sac Tumor
+14. Blank space
+
+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/bb5cb477-ad3c-464f-a75d-bbb4ae0d27c9)