text:   neural networks",8,"['Our new paper: ""Detecting hip fractures with radiologist-level performance using deep neural networks"". \n\n55,000 hip x-rays, big improvement over SOTA, AUC 0.994 (!). Huge team effort, especially from my exceptional co-author William Gale (an undergrad!)\n\n<LINK> <LINK>', ""@AndrewLBeam We discussed this a lot. In the end we decided to pre-print. Many good journals allow pre-prints, just no media. Also, what we do submit will have completely different experiments. Hopefully it doesn't bite us :)"", ""@AndrewLBeam Yeah, it seems to be a medical journal thing. There are other 'big' journals :)"", ""@DrDeclanORegan @DrHughHarvey I'm uncomfortable with cherry picked images in papers which is why there aren't any. The stats speak loudly IMO. But I'm happy to post some up tomorrow, the system does identify really subtle fractures. One that it got right took two CTs and an MRI before dx."", '@alexattia Not yet, we are still working on the project. The dataset is not public right now either, so any attempt to ""re-implement"" is unfortunately doomed. The network itself is just a DenseNet, there are many public implementations.', ""@alexattia We haven't, building a dataset for each new task is a huge effort. I see no reason why it wouldn't be achievable with good data, but that can be hard to achieve with other fractures (patients can be much more easily missed). I do know @enlitic did some work on wrist fractures."", '@alexattia @enlitic In our next experiments we will do some ablation. It depends what we mean by ""reasonable performance"". I think 95 AUC is totally achievable with less data, because the majority of fractures are obvious. Learning to deal with the subtle ones is much harder', '@DrDeclanORegan @DrHughHarvey Here is an example of the minimally displaced ones that it gets right. In the next paper we will include lots of images (it will be more justified with our experiments). https://t.co/MQLo4Cvp4X']",17,11,1885