/AI-Deep-Learning-Lab-2021

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

Primary LanguageJupyter NotebookMIT LicenseMIT

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