Thanks for the link. I myself have attempted using deep learning for segmentation with limited success with and without help from people with background in deep learning. I am not saying it's impossible but there is a lot of work left to do.
Yeah, with the renaissance that image analysis is undergoing in the last few years with DNNs, to say anything is impossible is showing a lack of imagination.
anyways, that's not my point. i'm saying deep learning for image analysis has been a huge success, and people should explore ways to apply this success to medical imaging.
but just for your info, neural networks are slowly being generalized to a lot of other challenges. look around for topics on recurrent neural networks, memory networks, reinforcement learning, etc etc. i don't think we've fully finished exploring the many ways neural networks can help solve life's challenges just yet.
Wow! Good. Thanks for this also. Last time I searched was a while ago, and I didn't find much. Glad to see progress. I will look into it "deeper" again.
Not everything is classification and there are problems better solved with other methods. For example I also haven't seeing anyone playing chess or go with DBN's. Also Compression, general programming...
Classification is a huge part of chess programs. You need to quickly evaluate a millions of boards to decide which side has the advantage. However, do to the well understood rules we can write efficient classifiers by hand.
Go is a much harder problem in large part because it's really hard to accurately classify which board is better off.
Teaching Deep Convolutional Neural Networks to Play Go: "Our convolutional neural networks can consistently defeat the well known Go program GNU Go... It is also able to win some games against state of the art Go playing program Fuego while using a fraction of the play time."http://arxiv.org/abs/1412.3409
