Data, notebooks, and articles associated with the RSNA AI Deep Learning Lab at RSNA 2021

RSNA

Last update: Dec 16, 2022

Related tags

Deep Learning AI-Deep-Learning-Lab-2021

Overview

RSNA AI Deep Learning Lab 2021

Intro

Welcome Deep Learners!

This document provides all the information you need to participate in the RSNA AI Deep Learning Lab. This set of classes provides a hands-on opportunity to engage with deep learning tools, write basic algorithms, learn how to organize data to implement deep learning and improve your understanding of AI technology.

The classes will be held in the RSNA AI Deep Learning Lab classroom, which is located in the Lakeside Learning Center, Level 3. Here's the schedule of classes. CME credit is available for each session.

Requirements

All lessons are designed to run in Google Colab, which is a free web-based version of Jupyter hosted by Google. You will need a Google account (eg, gmail) to use Colab. If you don't already have a Google account, please create one in advance at the account sign-up page. You can delete the account when you complete the lessons if you wish.

We recommend that you use a computer with a recent vintage processor running the Chrome browser.

Lessons

Lesson : Pneumonia Detection Model Building (Beginner friendly)

Lesson : MedNIST Exam Classification with MONAI (Beginner friendly)

Lesson : DICOM Data Wrangling with Python (Beginner friendly)

Lesson : CT Body Part Classification (Beginner friendly): Notebook #1, Notebook #2

Lesson : YOLO: Bounding Box Segmentation & Classification: Practice Notebook, Complete Notebook

Lesson : Integrating Genomic and Imaging Data with TCGA-GBM

Lesson : Generative Adversarial Networks

Lesson : Object Detection & Segmentation (Beginner friendly)

Lesson : Working with Public Datasets: TCIA & IDC (Beginner friendly)

Lesson : NLP: Text Classification with RNNs & Transformers: Notebook #1, Notebook #2

Lesson : Multimodal Fusion for Pulmonary Embolism Detection Using CTs and Patient EMR

Lesson : Data Processing & Curation for Deep Learning (Beginner friendly)

Lesson : Basics of NLP in Radiology (Beginner friendly)

Class Schedule

Date / Time	Class
Sun 10:30-11:30 am	MedNIST Exam Classification with MONAI - Beginner friendly
Sun 1:00-2:00 pm	DICOM Data Wrangling with Python - Beginner friendly
Sun 2:30-3:30 pm	CT Body Part Classification - Beginner friendly
Mon 9:30-10:30 am	YOLO: Bounding Box Segmentation & Classification
Mon 11:00 am-12:00 pm	Integrating Genomic and Imaging Data with TCGA-GBM
Mon 1:30-2:30 pm	Generative Adversarial Networks
Mon 3:00-4:00 pm	Object Detection & Segmentation
Mon 4:30-5:30 pm	Pneumonia Detection Model Building - Beginner friendly
Tue 11:00 am-12:00 pm	Working with Public Datasets: TCIA & IDC - Beginner friendly
Tue 3:00-4:00 pm	NLP: Text Classification with RNNs & Transformers
Wed 9:30-10:30 am	Pneumonia Detection Model Building - Beginner friendly; Repeat
Wed 11:00 am-12:00 pm	Working with Public Datasets: TCIA & IDC - Beginner friendly; Repeat
Wed 1:30-2:30 pm	Multimodal Fusion for Pulmonary Embolism Detection Using CTs and Patient EMR
Wed 4:30-5:30 pm	Data Processing & Curation for Deep Learning - Beginner friendly
Thu 11:00 am-12:00 pm	Basics of NLP in Radiology - Beginner friendly

Comments

IDC/TCIA tutorial materials

@wfwiggins this notebook is not complete, I am submitting a draft PR to know that we are working on it, but I don't think it should be merged yet. Hope this is ok.

opened by fedorov 2
Corrected the "random_state" issue in yolo notebook

Hello Dr. Wiggins. I removed that "random_state" argument in StratifiedGroupKFold command as it was causing errors in the notebook. It should run fine now.

opened by PouriaRouzrokh 1
YOLO - Final PR

Dear Dr. Wiggins,

This PR includes the final version of the notebooks and slides for the workshop.

Sorry for the multiple PRs during these weeks! Pouria

opened by PouriaRouzrokh 0
Commit nov15

Hi Dr. Wiggins,

We recently ran a pilot for our workshop in our own lab, and based on that, I modified our notebook and slideset a little. Most of the changes aim to make the workshop more time-efficient. I would be grateful if you can merge this PR.

BTW, I may take your time a couple of more times before RSNA with minor edits to the notebook or slides. I am still seeking feedback from a few colleagues on how I can improve the workshop.

Thank you, Pouria

opened by PouriaRouzrokh 0
Created using Colaboratory

This is Kirti Magudia's session "Data Processing & Curation for Deep Learning". Also adding a zip file for the sample data (link will need to be updated in colab notebook). Sample_DICOM.zip

opened by kirmag 0
Tensoerflow 1 deprecated in "RSNA 2021: TCIA and IDC"

WARNING: Tensorflow 1 is deprecated, and support will be removed on August 1, 2022. After that, %tensorflow_version 1.x will throw an error.

Your notebook should be updated to use Tensorflow 2. See the guide at https://www.tensorflow.org/guide/migrate#migrate-from-tensorflow-1x-to-tensorflow-2.

opened by tamarkashti 0

Data, notebooks, and articles associated with the RSNA AI Deep Learning Lab at RSNA 2021

Related tags

Overview

RSNA AI Deep Learning Lab 2021

Intro

Requirements

Lessons

Class Schedule

Comments

IDC/TCIA tutorial materials

Corrected the "random_state" issue in yolo notebook

YOLO - Final PR

Commit nov15

Created using Colaboratory

Tensoerflow 1 deprecated in "RSNA 2021: TCIA and IDC"

Owner

RSNA

Code associated with the paper "Deep Optics for Single-shot High-dynamic-range Imaging"

code associated with ACL 2021 DExperts paper

Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.

Train neural network for semantic segmentation (deep lab V3) with pytorch in less then 50 lines of code

A object detecting neural network powered by the yolo architecture and leveraging the PyTorch framework and associated libraries.

Code for the AI lab course 2021/2022 of the University of Verona

A code repository associated with the paper A Benchmark for Rough Sketch Cleanup by Chuan Yan, David Vanderhaeghe, and Yotam Gingold from SIGGRAPH Asia 2020.

Repo for the Video Person Clustering dataset, and code for the associated paper

A list of awesome PyTorch scholarship articles, guides, blogs, courses and other resources.

Listing arxiv - Personalized list of today's articles from ArXiv

Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

piSTAR Lab is a modular platform built to make AI experimentation accessible and fun. (pistar.ai)

All the code and files related to the MI-Lab of UE19CS305 course in sem 5

SAS output to EXCEL converter for Cornell/MIT Language and acquisition lab

RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

All-in-one Docker container that allows a user to explore Nautobot in a lab environment.

A trusty face recognition research platform developed by Tencent Youtu Lab

NHS AI Lab Skunkworks project: Long Stayer Risk Stratification