BI-RADS classification of breast cancer: a new pre-processing pipeline for deep models training



One of the main difficulties in the use of deep learning strate- gies in medical contexts is the training set size. While these methods need large annotated training sets, these datasets are costly to obtain in medical contexts and suffer from intra and inter-subject variability. In the present work, two new pre-processing techniques are introduced to improve a deep classifier performance. First, data augmentation based on co-registration is sug- gested. Then, multi-scale enhancement based on Difference of Gaussians is proposed. Results are accessed in a public mammogram database, the InBreast, in the context of an ordinal problem, the BI- RADS classification. Moreover, a pre-trained Convolutional Neural Network with the AlexNet architecture was used as a base classifier. The multi-class classification experiments show that the proposed pipeline with the Difference of Gaussians and the data augmentation technique outperforms using the original dataset only and using the original dataset augmented by mir- roring the images.

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Early-stage cancer treatment, driven by context of molecular imaging (ESTIMA)


IEEE International Conference on Image Processing (ICIP) 2018

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