Update README.md

README.md

@@ -1,3 +1,55 @@
 ---
 license: cc-by-4.0
+tags:
+- tiatoolbox
+- digital pathology
+- histology
+- kather
+- colorectal
+pipeline_tag: image-classification
 ---
+
+# ResNet50 trained on Kather100K (via TIA Toolbox)
+
+# Reusing the model
+
+Coming soon...
+
+# Dataset
+
+The Kather100K dataset can be found on Zenodo https://zenodo.org/record/1214456.
+
+# References
+
+```bibtex
+@inproceedings{he2016deep,
+  title={Deep residual learning for image recognition},
+  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={770--778},
+  year={2016}
+}
+@dataset{kather_jakob_nikolas_2018_1214456,
+  author       = {Kather, Jakob Nikolas and
+                  Halama, Niels and
+                  Marx, Alexander},
+  title        = {{100,000 histological images of human colorectal 
+                   cancer and healthy tissue}},
+  month        = apr,
+  year         = 2018,
+  publisher    = {Zenodo},
+  version      = {v0.1},
+  doi          = {10.5281/zenodo.1214456},
+  url          = {https://doi.org/10.5281/zenodo.1214456}
+}
+@article{pocock2022tiatoolbox,
+  title={TIAToolbox as an end-to-end library for advanced tissue image analytics},
+  author={Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and others},
+  journal={Communications medicine},
+  volume={2},
+  number={1},
+  pages={120},
+  year={2022},
+  publisher={Nature Publishing Group UK London}
+}
+```