1. | Byra M., Galperin M.♦, Ojeda-Fournier H.♦, Olson L.♦, O Boyle M.♦, Comstock C.♦, Andre M.♦, Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion, Medical Physics, ISSN: 0094-2405, DOI: 10.1002/mp.13361, Vol.46, No.2, pp.746-755, 2019Abstract:Purpose: We propose a deep learning-based approach to breast mass classification in sonographyand compare it with the assessment of four experienced radiologists employing breast imagingreporting and data system 4th edition lexicon and assessment protocol. Methods: Several transfer learning techniques are employed to develop classifiers based on a set of882 ultrasound images of breast masses. Additionally, we introduce the concept of a matching layer. The aim of this layer is to rescale pixel intensities of the grayscale ultrasound images and convertthose images to red, green, blue (RGB) to more efficiently utilize the discriminative power of theconvolutional neural network pretrained on the ImageNet dataset. We present how this conversioncan be determined during fine-tuning using back-propagation. Next, we compare the performance ofthe transfer learning techniques with and without the color conversion. To show the usefulness of ourapproach, we additionally evaluate it using two publicly available datasets. Results: Color conversion increased the areas under the receiver operating curve for each transferlearning method. For the better-performing approach utilizing the fine-tuning and the matching layer,the area under the curve was equal to 0.936 on a test set of 150 cases. The areas under the curves forthe radiologists reading the same set of cases ranged from 0.806 to 0.882. In the case of the two sepa-rate datasets, utilizing the proposed approach we achieved areas under the curve of around 0.890. Conclusions: The concept of the matching layer is generalizable and can be used to improve theoverall performance of the transfer learning techniques using deep convolutional neural networks. When fully developed as a clinical tool, the methods proposed in this paper have the potential to helpradiologists with breast mass classification in ultrasound. Keywords:BI-RADS, breast mass classification, convolutional neural networks, transfer learning, ultrasound imaging Affiliations:Byra M. | - | IPPT PAN | Galperin M. | - | Almen Laboratories, Inc. (US) | Ojeda-Fournier H. | - | University of California (US) | Olson L. | - | University of California (US) | O Boyle M. | - | University of California (US) | Comstock C. | - | Memorial Sloan-Kettering Cancer Center (US) | Andre M. | - | University of California (US) |
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