Yujing Zou's Projects
2D Vector-Quantized Auto-Encoder for compression of Whole-Slide Images in Histopathology
A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.
Delete the label from a whole-slide image
Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
Collection of awesome medical dataset resources.
:sparkles::sparkles:Latest Advances on Multimodal Large Language Models
A collection of research materials on SSL for non-sequential tabular data (SSL4NSTD)
A curated list of self-supervised multimodal learning resources.
Bioformer: an efficient BERT model for biomedical text mining
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Creating synthetic FHN data where each value corresponds to an equivalent state in CA
Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"
Code for the manuscript: "Single-cell imaging-AI based chromatin biomarkers for diagnosis and therapy evaluation in tumor patients using liquid biopsies". Code was developed by Daniel Paysan
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (CHIL 2020 Workshop)
The first Mini Project for McGill's COMP551 (Applied Machine Learning) Course.
Predict and analyze cellular automata using convolutional neural networks
COVID-Net model for COVID-19 detection on COVIDx dataset
WACV2021 - A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Deep learning enabled assessment of cardiac allograft rejection from endomyocardial biopsies- Nature Medicine
Code examples in pyTorch and Tensorflow for CS230
Public facing notes page
Dive into Deep Learning: an interactive deep learning book with code, math, and discussions
A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
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
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.