Large scale validation of the M5L lung CAD on heterogeneous CT datasets
Medical Physics2015Vol. 42(4), pp. 1477–1489
Citations Over TimeTop 10% of 2015 papers
E. López Torres, E. Fiorina, F. Pennazio, C. Peroni, M. Saletta, N. Camarlinghi, Maria Evelina Fantacci, P. Cerello
Abstract
The M5L performance on a large and heterogeneous dataset is stable and satisfactory, although the development of a dedicated module for GGOs detection could further improve it, as well as an iterative optimization of the training procedure. The main aim of the present study was accomplished: M5L results do not deteriorate when increasing the dataset size, making it a candidate for supporting radiologists on large scale screenings and clinical programs.
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