  Automated Lymph Node (LN) detection is an important clinical diagnostic task
but very challenging due to the low contrast of surrounding structures in
Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely
distributed locations. State-of-the-art studies show the performance range of
52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1
FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this
paper, we first operate a preliminary candidate generation stage, towards 100%
sensitivity at the cost of high FP levels (40 per patient), to harvest volumes
of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by
resampling 2D reformatted orthogonal views N times, via scale, random
translations, and rotations with respect to the VOI centroid coordinates. These
random views are then used to train a deep Convolutional Neural Network (CNN)
classifier. In testing, the CNN is employed to assign LN probabilities for all
N random views that can be simply averaged (as a set) to compute the final
classification probability per VOI. We validate the approach on two datasets:
90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs.
We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in
mediastinum and abdomen respectively, which drastically improves over the
previous state-of-the-art work.
