  We explore a solution for learning disease signatures from weakly, yet easily
obtainable, annotated volumetric medical imaging data by analyzing 3D volumes
as a sequence of 2D images. We demonstrate the performance of our solution in
the detection of emphysema in lung cancer screening low-dose CT images. Our
approach utilizes convolutional long short-term memory (LSTM) to "scan"
sequentially through an imaging volume for the presence of disease in a portion
of scanned region. This framework allowed effective learning given only
volumetric images and binary disease labels, thus enabling training from a
large dataset of 6,631 un-annotated image volumes from 4,486 patients. When
evaluated in a testing set of 2,163 volumes from 2,163 patients, our model
distinguished emphysema with area under the receiver operating characteristic
curve (AUC) of .83. This approach was found to outperform 2D convolutional
neural networks (CNN) implemented with various multiple-instance learning
schemes (AUC=0.69-0.76) and a 3D CNN (AUC=.77).
