Joseph Paul Cohen

I would start with a textbook or a course. Here are some: https://ocw.mit.edu/courses/nuclear-engineering/22-058-principles-of-medical-imaging-fall-2002/ https://www.coursera.org/learn/trunk-anatomy

The labels are designed to be hierarchy. This is shown in the table 1 from the paper: So they are supposed to co-occur because Pneumocystis is a Fungal Pneumonia. >...

Please check out this paper for a transfer learning approach and tasks to work on (and the clinical workflows that could benefit from tools): http://arxiv.org/abs/2006.11988 Also check out this library...

It should be a simple model to build. Just resize the images to 10x10 and train a logistic regression on it. Also, just look at the resolution of the image....

1. The files are not available to us as DICOMs (we are scraping publications because no one will share data) 2. The DICOM format is not as easy to load...

If the medical community would make DICOM images public then we could have this debate. But as it stands now that is not what is being made public. This dataset...

> At least put in some effort. Thanks for the advice! Please take a look though at the metadata file to find the outcome information you are looking for.

This work supports it: https://pubs.rsna.org/doi/10.1148/radiol.2020200432 and https://pubs.rsna.org/doi/10.1148/radiol.2020200642 And this recommends not using it: https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection But I believe it could help. Imagine if we run out of tests and then the...

I think focusing on making prognostic instead of diagnostic predictions makes more sense in terms of the need now.

True. This is a challenge to overcome. However, the models trained with a lot of data don't suffer from this issue though. Look at this example processed using a model...