Update README.md

README.md

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 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 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 and to integrate this model into research PACS (see [Results for VIBE](#results-for-vibe) section). 
-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) (see [Results for multi-sequence (T2, VIBE, and ADC)](#results-for-multi-sequence-t2-vibe-and-adc) section).
+In addition, we have looked at improving the segmentation performance using multi-sequence MR images (T2w, VIBE, and ADC) (see [Results for multi-sequence (T2, VIBE, and ADC)](#results-for-multi-sequence-t2-vibe-and-adc) section).
 
 ## Requirements
 Last checked and validated with fastMONAI version 0.3.9. Please ensure that you have the correct version of fastMONAI installed to guarantee the correct operation of the model.