Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay

Abstract

In this paper, we propose a Computer Assisted Diagnosis (CAD) system based on a deep Convolutional Neural Network (CNN) model, to build an end-to-end learning process that classifies breast mass lesions. We investigate the impact that has transfer learning when large data is scarce, and explore the proper way to fine-tune the layers to learn features that are more specific to the new data. The proposed approach showed better performance compared to other proposals that classified the same dataset.

arXiv comment6 pages, 2 figures, NIPS ML4H 2017: Machine Learning for Health Workshop at NIPS 2017, Long Beach, CA, United States, December 8, 2017

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