LiarLiu01's Stars
iperov/DeepFaceLab
DeepFaceLab is the leading software for creating deepfakes.
OpenVPN/openvpn
OpenVPN is an open source VPN daemon
linyiLYi/street-fighter-ai
This is an AI agent for Street Fighter II Champion Edition.
beamandrew/medical-data
BIMK/PlatEMO
Evolutionary multi-objective optimization platform
yuezk/chatgpt-mirror
A mirror of ChatGPT based on the gpt-3.5-turbo model.
cockroachai/cockroachai
一个简单的小程序,用于账号共享。免费但暂不开源。
xyhelper/chatgpt-share-server
xyhelper/chatgpt-mirror-server-deploy
chatgpt-mirror-server快速部署库文件
ppyyr/tailscale-android
Tailscale Android Client
MagicalMadoka/openai-signup-bot
openai注册机协议版
xyhelper/xyhelper-arkose-v2
xumengzi/Anti-Hundred-Poison
这是一款可以让你的百度清净的谷歌浏览器插件, 效果堪比 Adblock Plus, 欢迎试用
cockroachai/cockroachai-v2
一个简单的小程序,用于账号共享,免费开源
petiky/fkclaude
1:1 mirror of the Claude official website, completely replicated. It is not afraid of official resource updates and automatically synchronizes with official resources.
ivishalanand/Federated-Learning-on-Hospital-Data
A Federated Learning implementation to diagnose 2 acute inflammations of bladder.. This medical dataset truly needs privacy! Because we cannot divulge the sexually-transmitted diseases of patient
woairong/4C2019_Automatic-detection-algorithm-of-bladder-tumor-based-on-MRI
使用Python编程语言,基于tensorflow框架,采用AlexNet、LeNet、VGG、GooleNet、ZFNet和ResNet对1320张膀胱肿瘤图片进行训练,并在最终的170张测试集图片上测试得分,通过这次项目经历,强化对深度学习中计算机视觉领域主流框架的掌握。
2019ZSS/Bladder
医学影像处理
cgsaxner/UB_Segmentation
Code for automatic urinary bladder segmentation using Python and Tensorflow.
FelixZhangC/BladderSegmentation
The code for the challenge of The Third International Symposium on Image Computing and Digital Medicine (ISICDM 2019)
TerenceChen95/Bladder-Cancer-Stage-Detection
jckuri/BladderDataset
This machine learning system can diagnose 2 acute inflammations of bladder. The medical dataset contains features and diagnoses of 2 diseases of the urinary system: Inflammation of urinary bladder and nephritis of renal pelvis origin. This medical dataset truly needs privacy! Because we cannot divulge the sexually-transmitted diseases of patients. So, what we learned about PySyft and OpenMined is applied in this project. Federated learning will protect the privacy of datasets in each hospital and at the same time, a more robust machine learning model will benefit all hospitals. Why? Because the machine learning model generated in this project is 100% accurate; whereas human doctors can commit mistakes when diagnosing these 2 diseases.