Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks
This addresses the issue of missed or misdiagnosed breast masses in mammography screening, which is crucial for early cancer detection, though it is incremental as it applies existing deep learning methods to this domain.
The paper tackled the problem of classifying breast masses in mammograms as benign or malignant using Convolutional Neural Networks, achieving state-of-the-art results on the DDSM dataset and surpassing human performance.
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.