10 ms·
> This matters because if you have a method that is more computationally efficient, more easily interpreted, and more robust to extrapolation to out-of-domain d
by ramblenode 3y ago
> This matters because if you have a method that is more computationally efficient, more easily interpreted, and more robust to extrapolation to out-of-domain data, why in the world would you go with DL?
One reason we will see more deep learning in electrophysiology is simply because it allows one to abstract over a good deal of statistics and signal processing, which are hard. Deep learning is being used as a black box for those who can't or don't want to understand their data.
- Calavar 3y agoI agree with all your points. I think that training algorithms without understanding the underlying data is fundamentally a bad thing. This isn't NLP, where we're willing to put up with LLMs being inscrutable because they blow traditional algos out of the water. Accuracy with deep learning in ECG is moderately better than traditional methods, not paradigm shifting. And unfortunately the drawbacks of DL in ECG analysis are underanalyzed and underdiscussed. Most published DL ECG models that I've played around with have pretty significant failure modes that would preclude them from being used clinically. Of course, it's research, it's not meant to be clinical grade. But the failure modes aren't mentioned at all in the publications, and I've seen very little in the way of research into clamping down on some of these failure modes.