Update README.md

README.md

@@ -8,12 +8,13 @@ tags:
 # Endometrial cancer segmentation
 
 This repository contains weights and exported learner (encapsulates both the model architecture and its trained parameters) for a deep learning model designed to automate the segmentation of endometrial cancer on MR images. 
-Our model utilizes a Residual U-Net architecture, trained on data derived from the study [Automated segmentation of endometrial cancer on MR images using deep learning](https://link.springer.com/content/pdf/10.1038/s41598-020-80068-9.pdf).
-The primary objective of this repository is to reproduce the results reported in the study.
+Our VIBE model utilizes a Residual U-Net architecture, trained on data derived from the study [Automated segmentation of endometrial cancer on MR images using deep learning](https://link.springer.com/content/pdf/10.1038/s41598-020-80068-9.pdf).
+The primary objective of this repository is to reproduce the results reported in the study. In addition, we have looked at improving the segmentation performance using multi-sequence MR images (T2w, VIBE, and ADC) as reported in the study [Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network](https://www.nature.com/articles/s41598-021-93792-7).
+
 
 ## Requirements
 
-Last checked and validated with fastMONAI version 0.3.2. Please ensure that you have the correct version of fastMONAI installed to guarantee the correct operation of the model.
+Last checked and validated with fastMONAI version 0.3.4. Please ensure that you have the correct version of fastMONAI installed to guarantee the correct operation of the model.
 
 ## Usage