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. Author manuscript; available in PMC: 2020 Jul 7.
Published in final edited form as: IEEE Trans Med Imaging. 2015 Sep 28;35(5):1170–1181. doi: 10.1109/TMI.2015.2482920

Fig. 14.

Fig. 14.

Comparison of the FROC performance of when training a ConvNet with 2D, 2.5D and 3D inputs of the original (“ORIG”) or augmented (“AUG”) CT data. In the “ORIG” setting, 2D ConvNet shows the best generalized testing FROC result, followed by 3D and 2.5D ConvNets. The 2.5D approach using aggregation of random observations (“AUG”) in both training (Left) and testing (Right), out-performs both 2D and 3D approaches on the original data at the 3 FPs/patient level. The 2.5D ConvNet trained on augmented data overall performs comparably to a more computationally expensive 3D ConvNet approach on augmented 3D inputs. In brief, the evaluated 2.5D “AUG” ConvNet is chosen as the best trade-off lymph node detection model between effectiveness and efficiency.