Update README.md

update new work

Author: yhygao

README.md

@@ -3,7 +3,8 @@ Official implementation of [UTNet: A Hybrid Transformer Architecture for Medical
 
 
 ## Update
-Our new paper, the improved version of UTNet: UTNetV2, is released on Arxiv: [A Multi-scale Transformer for Medical Image Segmentation: Architectures, Model Efficiency, and Benchmarks](https://arxiv.org/abs/2203.00131). The UTNetV2 has an improved architecture for 2D and 3D setting. We also provide a more general framework for CNN and Transformer comparison, including more dataset support, more SOTA model support, see in our new repo：https://github.com/yhygao/CBIM-Medical-Image-Segmentation
+* Our new work, Hermes, has been released on arXiv: [Training Like a Medical Resident: Universal Medical Image Segmentation via Context Prior Learning](https://arxiv.org/pdf/2306.02416.pdf). Inspired by the training of medical residents, we explore universal medical image segmentation, whose goal is to learn from diverse medical imaging sources covering a range of clinical targets, body regions, and image modalities. Following this paradigm, we propose Hermes, a context prior learning approach that addresses the challenges related to the heterogeneity on data, modality, and annotations in the proposed universal paradigm. Code will be released at https://github.com/yhygao/universal-medical-image-segmentation.
+* Our new paper, the improved version of UTNet: UTNetV2, is released on Arxiv: [A Multi-scale Transformer for Medical Image Segmentation: Architectures, Model Efficiency, and Benchmarks](https://arxiv.org/abs/2203.00131). The UTNetV2 has an improved architecture for 2D and 3D setting. We also provide a more general framework for CNN and Transformer comparison, including more dataset support, more SOTA model support, see in our new repo：https://github.com/yhygao/CBIM-Medical-Image-Segmentation
 
 Data preprocess code uploaded.