I have a question. I know nothing about ML and neural networks so I'm going to excuse myself if the answer to this may be obvious.
The specificity of CT when interpreted by humans is reported to be <56% [1]. The specificity for this model seems to be around 80%, which looks too good to me (I did 79/(79+19)=0.8, taking the numbers from the table at the bottom in OP).
Is it that the non-Covid-19 scans in the training data were easier to recognize than the ones doctors see every day or is the model so much better than humans at recognizing non-Covid-19 cases? If it was the latter, the reduced sensitivity (human: 96% [1], model: 85%) would not look bad at all, right?
The specificity of CT when interpreted by humans is reported to be <56% [1]. The specificity for this model seems to be around 80%, which looks too good to me (I did 79/(79+19)=0.8, taking the numbers from the table at the bottom in OP).
Is it that the non-Covid-19 scans in the training data were easier to recognize than the ones doctors see every day or is the model so much better than humans at recognizing non-Covid-19 cases? If it was the latter, the reduced sensitivity (human: 96% [1], model: 85%) would not look bad at all, right?
[1] https://pubs.rsna.org/doi/full/10.1148/radiol.2020201709