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That's a great point. Fundamentally, if you look at something like a CNN, what it's really doing is producing a feature descriptor based on the input image. One can easily use that feature descriptor in a classic SVM, alongside (or instead of) SoftMax.

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Yup, in fact, the universal feature extraction is what allows imagenet pretraining to work well on lung cancer images.

One nitpick though, ConvNets can absolutely be used to do "thinking" and more than just feature extraction. For example, fully convolutional networks can be extremely competitive with FC-layer based nets.