12 March 2018 End-to-end breast ultrasound lesions recognition with a deep learning approach
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Abstract
Existing methods for automated breast ultrasound lesions detection and recognition tend to be based on multi-stage processing, such as preprocessing, filtering/denoising, segmentation and classification. The performance of these processes is dependent on the prior stages. To improve the current state of the art, we have proposed an end-to-end breast ultrasound lesions detection and recognition using a deep learning approach. We implemented a popular semantic segmentation framework, i.e. Fully Convolutional Network (FCN-AlexNet) for our experiment. To overcome data deficiency, we used a pre-trained model based on ImageNet and transfer learning. We validated our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions. We assessed the performance of the model using the following split: 70% for training data, 10% for validation data, and 20% testing data. The results show that our proposed method performed better on benign lesions, with a Dice score of 0.6879, when compared to the malignant lesions with a Dice score of 0.5525. When considering the number of images with Dice score > 0.5, 79% of the benign lesions were successfully segmented and correctly recognised, while 65% of the malignant lesions were successfully segmented and correctly recognised. This paper provides the first end-to-end solution for breast ultrasound lesion recognition. The future challenges for the proposed approaches are to obtain additional datasets and customize the deep learning framework to improve the accuracy of this method.
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Moi Hoon Yap, Moi Hoon Yap, Manu Goyal, Manu Goyal, Fatima Osman, Fatima Osman, Ezak Ahmad, Ezak Ahmad, Robert Martí, Robert Martí, Erika Denton, Erika Denton, Arne Juette, Arne Juette, Reyer Zwiggelaar, Reyer Zwiggelaar, } "End-to-end breast ultrasound lesions recognition with a deep learning approach", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1057819 (12 March 2018); doi: 10.1117/12.2293498; https://doi.org/10.1117/12.2293498
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