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There is this very popular idea these days called Generative Adversarial Networks. An essential component of this is to training something called a discriminative network. One which discriminated between a real image and a fake image. In our case it could be something like an image of virus A vs an image of Virus B. Doing Guided backpropagation on this could be really interesting. ref: http://arxiv.org/pdf/1406.2661v1.pdf
Another interesting approach could be finding a pretrained Neural Network that is already giving really good error on the IMageNet dataset. These networks produce really nice guided backpropogation features. It would be interesting to train more layers on top of it or eliminate the top half of the network and train some more layers on the bottom half for our virus problem . Then maybe doing guided backpropogation could really help us.
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