Name: Luan Haijing
Type: User
Company: Computer Network Information Center of the Chinese Academy of Sciences
Bio: I am Luan Haijing, a student in Computer Network Information Center of the Chinese Academy of Sciences. I want to study in Github and try to make some contribut
Location: Computer Network Information Center of the Chinese Academy of Sciences
Luan Haijing's Projects
AffinityNet with feature attention layer and kNN attention pooling layer for few-shot semi-supervised learning
A PyTorch implementation of the Transformer model in "Attention is All You Need".
Implementation of Attention-based Deep Multiple Instance Learning in PyTorch
Image data augmentation on-the-fly by add new class on transforms in PyTorch and torchvision.
深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI
A topic-centric list of HQ open datasets.
A ledger for private and secure peer to peer machine learning
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory
A Java package for non-invasive cancer diagnosis using methylation profiles of cell-Free DNA.
The manuscript has been accepted in TMI.
cfDNApipe: A comprehensive quality control and analysis pipeline for cell-free DNA high-throughput sequencing data
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
This is the repository for paper titled as "Convolutional neural network models for cancer type prediction based on gene expression".
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Official implementation of CrossViT. https://arxiv.org/abs/2103.14899
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被55个国家的300所大学用于教学。
deep learning for image processing including classification and object-detection etc.
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
深度学习与PyTorch入门实战视频教程 配套源代码和PPT
A deep learning framework for detecting lesions in CT scans from Deep Lesion dataset
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06
WACV18 paper "DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification"
A Multi-Omics Scalable and Interpretable Deep Learning Framework and Application in Cancer Survival Prediction