In the spirit of the now extremely successful Geospatial Deep Learning resources & study group started by @daveluo and Time series/ sequential data study group started by @oguiza, this discussion thread is dedicated as a resource sharing and gathering space for people enthusiastic about applying deep learning techniques and what we have learned in Part 1 and will learn in Part 2 to solve the challenges we face in medicine and healthcare.
To get things started, here is a list of resources and recent relevant posts. This post should be made a wiki soon and you will be able to add your content in no time.
If any of you are enthusiastic about healthcare and want to focus on related applications when taking Part 1/2, please leave a reply below and tell us what you want to work on. It would be super exciting to work with a group of fellow fastai students to understand, implement, and possibly beat the papers in this field!
A guide to deep learning in healthcare
- Published in Nature by a group of researchers from Stanford University and Google.
- This little interview with Dr. Alexandre Cadrin-Chenevert by @init_27
- American College of Radiology Data Science Institute TOUCH-AI Use Cases: a list of vetted use cases for AI in medical imaging
Work by Fellow Fastai Students
Please kindly put your work here! Remember to @ yoruself so people can know who you are.
- Fastai scans @renato is working on this for medical images, it have dataloaders and models for 3d images with fastai api.
- fastai for genomics @micpie: starter notebook from “A primer on deep learning in genomics” in plain pytorch and with fastai
- Deep Learning with Magnetic Resonance and Computed Tomography Images @jcreinhold: An overview of working with deep learning models for MR and CT image applications
Clinically applicable deep learning for diagnosis and referral in retinal disease
- A group of researchers mostly from Deep Mind, London. Here is an accompanying website announcement that might provide a more friendly overview of the paper.
- Dermatologist-level classification of skin cancer with deep neural networks
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
- Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
- Identifying facial phenotypes of genetic disorders using deep learning
- Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
You could always find more here in the medicine category of Papers with Code.
MURA (musculoskeletal radiographs)
- “MURA (musculoskeletal radiographs) is a large dataset of bone X-rays. Algorithms are tasked with determining whether an X-ray study is normal or abnormal.”
- “CheXpert is a large dataset of chest X-rays and competition for automated chest x-ray interpretation, which features uncertainty labels and radiologist-labeled reference standard evaluation sets.”
- RSNA Pneumonia Detection Challenge
2018 Data Science Bowl
- “Find the nuclei in divergent images to advance medical discovery”
Kaggle Histopathologic Cancer Detection
- " Identify metastatic tissue in histopathologic scans of lymph node sections"
- Here is a giant medical dataset repo in Github.
Stanford Machine Learning Group
- Led by Andrew Ng
- Though with a very general name, it focuses very specifically on healthcare.
- Hosted some very interesting medical image challenges with very detailed and user-friendly guide on how to participate.
Applied Deep Learning in Radiology, Oncology and Pathology
- A series of conference tutorials that use PyTorch and deep learning techniques to solve classification, segmentation, and object detection tasks in medicine.
- Discord study/working group for Fast.AI people interested in medical collaboration, paper discussion, and problem solving.
Note that this is a forum wiki thread, so you all can edit this post to add/change/organize info to help make it better! To edit, click on the little edit icon at the bottom of this post. Here’s a pic of what to look for: