update README: uni and conch

docs/README.md

@@ -16,6 +16,7 @@ Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imag
 © [Mahmood Lab](http://www.mahmoodlab.org) - This code is made available under the GPLv3 License and is available for non-commercial academic purposes. 
 
 ## Updates:
+* **03/19/2024**: We are releasing [UNI](https://github.com/mahmoodlab/UNI) and [CONCH](https://github.com/mahmoodlab/CONCH), a pair of SOTA pretrained encoders that produce strong representations for histopathology images and enhance performance on various computational pathology workflows, including the MIL-based CLAM workflow. Stay tuned for more updates on how to use these encoders with CLAM in the next few days.
 * 05/24/2021: Script for heatmap visualization now available via **create_heatmaps.py**, with the configuration template located in **heatmaps/configs**. See [Heatmap visualization for demo and instructions.](#Heatmap-Visualization)
 * 03/01/2021: New, fast patching/feature extraction pipeline is now available. **TL;DR:** since CLAM only requires image features for training, it is not necessary to save the actual image patches, the new pipeline rids of this overhead and instead only saves the coordinates of image patches during "patching" and loads these regions on the fly from WSIs during feature extraction. This is significantly faster than the old pipeline and usually only takes 1-2s for "patching" and a couple minutes to featurize a WSI. To use the new pipeline, make sure you are calling **create_patches_fp.py** and **extract_features_fp.py** instead of the old **create_patches.py** and **extract_features.py** scripts.