TY - GEN
T1 - Analyzing Neural Networks Applied to an Anatomical Simulation of the Breast
AU - Abbey, Craig K.
AU - Sengupta, Sourya
AU - Zhou, Weimin
AU - Badal, Andreu
AU - Zeng, Rongping
AU - Samuelson, Frank W.
AU - Eckstein, Miguel P.
AU - Myers, Kyle J.
AU - Anastasio, Mark A.
AU - Brankov, Jovan G.
N1 - This work was supported by the NIH through research grants (R01-EB025829 and R01 EB026427). The content of this proceedings paper is solely the responsibility of the authors and does not represent the institutional views of any funding agency.
PY - 2022
Y1 - 2022
N2 - In the last 5 to 10 years, there has been an enormous increase in the interest and use of network models in imaging. These are being considered for numerous imaging applications, including denoising, decision support, learned-feature selection, and many others. Network models "learn" solutions to imaging problems from labelled training data and an elaborate training regime. When a successful model is developed, it represents a computational algorithm for performing some task of interest. But it also encodes a solution to an imaging problem that may be intractable by conventional analytical means. Network models are therefore of interest for how they formulate a solution to a problem of interest. This work focuses on that process. We present two case studies in the analysis of neural networks. The first consists of a denoising network for digital breast tomosynthesis (DBT) images developed using a complex anatomical simulation of breast tissues and realistic x-ray transport physics. The second looks at a lesion detection network, also for DBT images, based on the same anatomical simulation model. For the denoising network, we find that it is very well represented by a linear operation that is effectively a Gaussian convolution kernel. The detection filter appears to be locally linear, but the filter profile appears to depend on what stimulus is used to probe the network. There does not appear to be any clear structure in quadratic components from reverse correlation. Overall, this study shows how regression and reverse-correlation techniques can be used to analyze network models. 2022 SPIE.
AB - In the last 5 to 10 years, there has been an enormous increase in the interest and use of network models in imaging. These are being considered for numerous imaging applications, including denoising, decision support, learned-feature selection, and many others. Network models "learn" solutions to imaging problems from labelled training data and an elaborate training regime. When a successful model is developed, it represents a computational algorithm for performing some task of interest. But it also encodes a solution to an imaging problem that may be intractable by conventional analytical means. Network models are therefore of interest for how they formulate a solution to a problem of interest. This work focuses on that process. We present two case studies in the analysis of neural networks. The first consists of a denoising network for digital breast tomosynthesis (DBT) images developed using a complex anatomical simulation of breast tissues and realistic x-ray transport physics. The second looks at a lesion detection network, also for DBT images, based on the same anatomical simulation model. For the denoising network, we find that it is very well represented by a linear operation that is effectively a Gaussian convolution kernel. The detection filter appears to be locally linear, but the filter profile appears to depend on what stimulus is used to probe the network. There does not appear to be any clear structure in quadratic components from reverse correlation. Overall, this study shows how regression and reverse-correlation techniques can be used to analyze network models. 2022 SPIE.
KW - Breast Phantoms
KW - Neural Networks
KW - Reverse Correlation
UR - http://www.scopus.com/inward/record.url?scp=85131864256&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85131864256&partnerID=8YFLogxK
U2 - 10.1117/12.2612614
DO - 10.1117/12.2612614
M3 - Conference contribution
AN - SCOPUS:85131864256
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2022
A2 - Mello-Thoms, Claudia R.
A2 - Mello-Thoms, Claudia R.
A2 - Taylor-Phillips, Sian
PB - SPIE
T2 - Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment
Y2 - 21 March 2022 through 27 March 2022
ER -