Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks

Abstract

Mammography is the most widely used method to screen breast cancer. Because of its mostly manual nature, variability in mass appearance, and low signal-to-noise ratio, a significant number of breast masses are missed or misdiagnosed. In this work, we present how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using a combination of transfer learning, careful pre-processing and data augmentation to overcome limited training data. We achieve state-of-the-art results on the DDSM dataset, surpassing human performance, and show interpretability of our model.

NIPS 2016 ML4HC Workshop

References in corpus (2)

• Adam: A Method for Stochastic Optimization
• Caffe: Convolutional Architecture for Fast Feature Embedding

Cited by in corpus (7)

• SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis
• High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
• Multitask Classification and Segmentation for Cancer Diagnosis in Mammography
• Breast Mass Detection with Faster R-CNN: On the Feasibility of Learning from Noisy Annotations
• Expert identification of visual primitives used by CNNs during mammogram classification
• Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection
• Adversarial Augmentation for Enhancing Classification of Mammography Images