With high resolution images you need large amount of layers to for the later layers to be able to 'see' the whole picture. But such a network would likely be un

With high resolution images you need large amount of layers to for the later layers to be able to 'see' the whole picture. But such a network would likely be unreasonably large for such large inputs, you'd have to look for distributed model solutions. So it kind of depends on what you are trying to extract from such large images. If it's macroscopic features then you should just downsample your image to something reasonable and feed it into some pretrained network like Alexnet or VGGnet. If you want microscopic feature detection, but don't care about macro features, then you should use a shallower convnet. If you insist on having both but the resulting network can't be stored in memory and distributed system is not an option, you might want to look into using recurrent networks with visual attention. These sort of look at manageable chunks of the image and decide where else to look based on what they've seen so far.