UK Biobank is a British clinical study containing over 40 000 Magnetic Resonance Images (MRI) with 100 000 MRI planned of participants aged 44-82 as well as a large amount of related medical data. Analyzing these images with a neural network to find relations between the information in an MRI image and various medical data could lead to interesting medical revelations. While other studies usually focus on improving the network architecture, we instead propose a method to get targeted information out of full body MRI images. This is done by sampling various sub-volumes of the full body images and making a collage specifically tailored to the problem at hand before feeding them to a ResNet50 based network. The images are further analyzed using saliency analysis in order to gain information on what regions the network found important. This method was attempted on a variety of medical data including age, kidney volume, liver fat percentage, and heart volume. The method is used both as a way to increase information density in the input images as well as restricting information, such that we can see how well the network can predict about some medical data point from only some part of the body.The collages are able to increase the information in the images while the more complex representation and non-continuous representation does not cause problems for the network. These collages are also conducive to getting clearer and sharper saliency maps, which may give interesting medical information by showing what regions the network considers relevant. This may reveal otherwise difficult to notice relations between the information in the MRI images and medical information.