annchou Goto Github PK

af_discovery

AF_Discovery: AI4GoodLab 2023. Team Toronto1. Machine Learning Pipeline to segment Ultrasound (US) images for Amniotic Fluid (AF). But due to lack of ground truth dataset with AF. Our model is trained for Head Circumference, and will transfer learning when we can access the right data set. [1] UNet (CNN) on US [2] Random Forest Prediction to HC

angular-cli

CLI tool for Angular

annchou.github.io

Art of AnnChou

annreflectionarts

Webpage of AnnReflectionArts

bclibrary

Slidedeck for "The Digital Fab Lab at UVic: Between English and Visual Arts"

bootcamp

Teaching and curriculum materials for the Future Savvy bootcamp

bootstrap_ex_week1

bot_tutorial

boxwrap2djs

A Javascript port of boxwrap2d, a processing wrapper for Box2D

cover-card

:necktie: Online business card with a minimal landing page theme for any social media or online presence account: fork, edit, and go!

datasciencecoursera

datascientisttoolbox

datasharing

The Leek group guide to data sharing

dhsi2015

Repository for Physical Computing and Fabrication Course at DHSI 2015

dltk_practice

Deep Learning Toolkit for Medical Image Analysis

emoaware

EmoAware recognizes five emotions from acoustic features of speech. Based on Vokaturi SDK

fpus23

DL and dataset by FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features

friendlyeats-web

fullstack-course1-module1

glumonth

h1-af

Ann-K working repo for AI4Goodlab -- aim for Segmentation of Amniotic Fluid, by machine learning on Ultrasound image - UNet. But we are modular approach. my current contribution is on doing a random forest regression between pixel size and head circumstane, and manual annotation for fetal ultrasound that we can retrieeve.

hdtmt

A "How Did They Make That?", by undergrads for undergrads

heit

helt

ht-test

ht-test-py

intro-to-gh

introtopandas

This repo contains materials to 'Introduction to Pandas' talk.

ipdln-2018-hackathon

Repository to accompany a hackathon at IPDLN conference at Banff, Sep 2018

learningdatascience

LearningDataScience