yangyunfeng-cyber

计算机在读博士生。研究领域包括但不限于: 机器学习；深度学习；医学影像放射组学方法研究，可解释人工智能算法在医疗决策领域的运用。

Shang Hai, China

Pinned Repositories

LeetCode-Exercise
the solution of leetcode
Language:Python00
Medical_preprocess
There are some useful code for preprocessing medical image
Language:Python0 1 00
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.
00
TransLiteUNet
Source Code of the Paper: 1M Parameters Suffice: Uniting CNNs and Transformers for Ultra-Lightweight 3D Medical Image Segmentation
0 1 00
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。
Language:Python9 1 00

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。
Language:Python9 1 00
yangyunfeng-cyber/LeetCode-Exercise
the solution of leetcode
Language:Python00
yangyunfeng-cyber/Medical_preprocess
There are some useful code for preprocessing medical image
Language:Python0 1 00
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.
00
yangyunfeng-cyber/TransLiteUNet
Source Code of the Paper: 1M Parameters Suffice: Uniting CNNs and Transformers for Ultra-Lightweight 3D Medical Image Segmentation
0 1 00