yangyunfeng-cyber
Ph.D. in Medical Artificial Intelligence from the Chinese Academy of Sciences, specializing in radiomics, pathomics, genomics, and research on interpretable AI
Shang Hai, China
Pinned Repositories
DICOM_Preprocess_Codes
There are some useful code for preprocessing medical image
Image_Heterogeneity_Assessment
建立一种可解释的图像异质性评估模型，来量化评估肝肿瘤的异质性。
LeetCode-Exercise
the solution of leetcode
Machine-Learning-Programming-Tasks
This project provides post class programming task code for machine learning courses
PCNSL-GBM-Multimodal-Fusion-Classifier
The aim of this project is to develop an image classification model using MRI images, integrating three methods: CNN, radiomics, and transformer. The primary objective is to enhance the accuracy of preoperative differentiation between PCNSL and GBM, two types of malignant brain tumors.
TransLiteUNet
Source Code of the Paper: 1M Parameters Suffice: Uniting CNNs and Transformers for Ultra-Lightweight 3D Medical Image Segmentation
Useful-DL-Projects-for-Exercise
一些可以复现的经典网络项目，仓库内所有代码均为复现且注释过的，不含BUG，若无法复现请考虑是否与项目第三方库版本有出入。
yangyunfeng-cyber's Repositories
yangyunfeng-cyber/Useful-DL-Projects-for-Exercise
一些可以复现的经典网络项目，仓库内所有代码均为复现且注释过的，不含BUG，若无法复现请考虑是否与项目第三方库版本有出入。
yangyunfeng-cyber/DICOM_Preprocess_Codes
There are some useful code for preprocessing medical image
yangyunfeng-cyber/Image_Heterogeneity_Assessment
建立一种可解释的图像异质性评估模型，来量化评估肝肿瘤的异质性。
yangyunfeng-cyber/LeetCode-Exercise
the solution of leetcode
yangyunfeng-cyber/Machine-Learning-Programming-Tasks
This project provides post class programming task code for machine learning courses
yangyunfeng-cyber/PCNSL-GBM-Multimodal-Fusion-Classifier
The aim of this project is to develop an image classification model using MRI images, integrating three methods: CNN, radiomics, and transformer. The primary objective is to enhance the accuracy of preoperative differentiation between PCNSL and GBM, two types of malignant brain tumors.
yangyunfeng-cyber/TransLiteUNet
Source Code of the Paper: 1M Parameters Suffice: Uniting CNNs and Transformers for Ultra-Lightweight 3D Medical Image Segmentation