When humans identify objects in images, context is an important cue; a cheetah is more likely to be a domestic cat when a television set is recognised in the background. Similar principles apply to the analysis of medical images. The detection of diseases that manifest unilaterally in symmetrical organs or organ pairs can in part be facilitated by a search for symmetrical discrepancies in or between the organs in question. During a mammographic exam, images are recorded of each breast and absence of a certain structure around the same location in the contralateral image will render the area under scrutiny more suspicious and conversely, the presence of similar tissue less so. In this paper, we present a fusion scheme for a deep Convolutional Neural Network (CNN) architecture with the goal to optimally capture such asymmetries. The method is applied to the domain of mammography CAD, but can be relevant to other medical image analysis tasks where symmetry is important such as lung, prostate or brain images.
Thijs Kooi and Nico Karssemeijer, "Deep learning of symmetrical discrepancies for computer-aided detection of mammographic masses," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341J (Presented at SPIE Medical Imaging: February 16, 2017; Published: 3 March 2017); https://doi.org/10.1117/12.2254586.
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