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It contradicts your statement: "it's not as simple as having someone at Hospital X download a pretrained model in Tensorflow and train its last few layers on so
by bodono 7y ago
It contradicts your statement: "it's not as simple as having someone at Hospital X download a pretrained model in Tensorflow and train its last few layers on some CT scans"
Because in this case it was as easy as taking a model from a totally different modality and retraining the first (in this case) few layers to accommodate the new device. Furthermore the original training used 15k scans and the retraining only required 152 scans. This is totally reasonable and clear evidence of transfer and generalization. Moreover, even human operators require retraining on new devices!
- YeGoblynQueenne 7y agoMy Tensorflow comment was a bit unclear. I meant that you can't just download a generic model like the kind that is readily available, e.g. one trained on ImageNet or CIFAR etc, and expect that you can retrain it easily and get a diagnostic tool that is competitive with an expert. The models in the paper you link were specifically trained on medical imaging data. My point is that you need a lot of work to make this work even for one hospital, let alone scale to many, even more so scale at the level of a national health service. I don't see that the paper you link contradicts this. Edit: if I may summarise: I said "it's not simple" not "you can't do it". Transfer learning is not generalisation to unseen data. If the pre-trained model and the end model don't have any common instances it doesn't work [Edit: "don't have any instances with a common feature space" is more clear]. Also, you're talking about generalisation to new devices. My understanding is that this is only one aspect of the difficulties with scaling image recognition for medical diagnoses to data from different sites.