Medical data is horrible to work with, but deep learning can quickly and efficiently solve many of these problems.
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Since the CheXNet paper came out in November 2017 I have been communicating with the author team. I'm finally ready to review the paper. Some of the things I found out surprised me.
A couple of weeks ago, I mentioned I had some concerns about the ChestXray14 dataset. I said I would come back when I had more info, and since then I have been digging into the data. I've talked with Dr Summers via email a few times as well. Unfortunately, this exploration has only increased my concerns about the dataset.
Deep learning research in medicine is a bit like the Wild West at the moment; sometimes you find gold, sometimes a giant steampunk spider-bot causes a ruckus. This has derailed my series on whether AI will be replacing doctors soon, as I have felt the need to focus a bit more on how to assess … Continue reading Do machines actually beat doctors? ROC curves and performance metrics
Today I want to look at two papers which tell us something very useful about medical AI, particularly if we are trying to predict the future of medicine.
Just a quick note: If you are in South Australia and you are interested in radiology or research, or even radiology research, feel free to contact me. I can answer any questions you have or maybe even connect you with researchers who need help. And if anyone is willing to give me feedback on my teaching materials, … Continue reading Interested in radiology or research?
Welcome to the first post of my blog! It feels a bit self-indulgent, but documenting my path into academia might help others who may find themselves in the same situation I was a bit over two years ago: a medical doctor who has never considered a career in research. To explain that, a bit about how … Continue reading First steps as a clinician researcher