Today, I will share the improved model GER-UNet, which is based on the 2020 paper “Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation.” By understanding the ideas behind this model, similar improvements can be made based on VNet. 1. Limitations of Conventional Convolutional Networks 1. Conventional convolutional neural networks can only utilize … Read more
Click the above“Beginner Learning Vision” and select toStar or “Pin” Important information delivered first time Source｜Daniel Liu@Zhihu, https://zhuanlan.zhihu.com/p/138555896 Multi-scale self-guided attention for medical image segmentation The method in this paper is the optimal method for medical image segmentation on the CHAOS MRI Dataset published in 2019, with a final Dice score of 86.75. Introduction: Views … Read more
Follow our WeChat public account to discover the beauty of CV technology This article shares the NeruIPS 2023 paper SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process, which achieves high-precision image segmentation through diffusion. Details are as follows: Paper link: https://arxiv.org/abs/2312.12425 Open-source code: https://github.com/MengyuWang826/SegRefiner Background Introduction Although image segmentation has been widely researched and … Read more
Last time, I published an article titled “Building Your Own Simple Knowledge Base with Ollama,” and I found that many users encountered various issues during usage. I also faced similar problems. Particularly, after importing an article into the MaxKB knowledge base and asking questions within the application, the answers were completely off-topic and did not … Read more
Datawhale Insights Author: Song Zhixue, Datawhale Member Hello everyone, I am not a fan of garlic and ginger. Next, I will guide you step by step to implement a simple RAG model, which is a simplified version of RAG, called Tiny-RAG. Tiny-RAG is a simplified version of RAG that only includes the core functions of … Read more