Pinned Repositories
3d-mri-brain-tumor-segmentation-using-autoencoder-regularization
Keras implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. (https://arxiv.org/abs/1810.11654).
3D_RFUltrasound_Reconstruction
3D Volume Reconstruction of Raw Ultrasound Radiofrequency Data
AD_Prediction
毕业设计项目-基于深度学习的阿兹海默症早期诊断辅助系统设计与实现
Ai-Learn
人工智能学习路线图,整理近200个实战案例与项目,免费提供配套教材,零基础入门,就业实战!包括:Python,数学,机器学习,数据分析,深度学习,计算机视觉,自然语言处理,PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域
Aiwenbeauty
ant-back
:rocket: react后台,后台管理系统
Approximate-2D-RF-Imaging
This repository is for imaging by inverse reconstruction of the image by received RF signal using different methods: Compressed sensing, Maximum Entropy, and Total Variation. The data generated here is using ray-tracing based models, not as accurate as CST simulation generated data.
awesome-zero-shot-learning
A curated list of papers, code and resources pertaining to zero shot learning
brain-segmentation
Deep learning based skull stripping and FLAIR abnormality segmentation in brain MRI using U-Net
ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。
Aiwenbeauty's Repositories
Aiwenbeauty/brain-segmentation
Deep learning based skull stripping and FLAIR abnormality segmentation in brain MRI using U-Net
Aiwenbeauty/py-faster-rcnn
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
Aiwenbeauty/VerasonicsVantageHardware-GUI
To mimic the functions of signal generator and osscilliscope with Verasonics Vantage Hardware.
Aiwenbeauty/KGQA_HLM
基于知识图谱的《红楼梦》人物关系可视化及问答系统
Aiwenbeauty/Image
Medical ultrasound image processing.Carotid ultrasoung segmentation using RF data.
Aiwenbeauty/koa-demos
A collection of simple demos of Koa
Aiwenbeauty/Approximate-2D-RF-Imaging
This repository is for imaging by inverse reconstruction of the image by received RF signal using different methods: Compressed sensing, Maximum Entropy, and Total Variation. The data generated here is using ray-tracing based models, not as accurate as CST simulation generated data.