High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

By Krzysztof J. Geras, Stacey Wolfson, S. Gene Kim, Linda Moy, Kyunghyun Cho. 2017

245 citations recorded by the source corpus.

Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental differences between these two types of images. Specifically, fine details are necessary for detection in medical images, unlike in natural images where coarse structures matter most. This difference makes it inadequate to use the existing network architectures developed for natural images, because they work on heavily downscaled images to reduce the memory requirements. This hides details necessary to make accurate predictions. Additionally, a single exam in medical imaging often comes with a set of views which must be fused in order to reach a correct conclusion. In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images. We evaluate it on large-scale mammography-based breast cancer screening (BI-RADS prediction) using 886,000 images. We focus on investigating the impact of the training set size and image size on the prediction accuracy. Our results highlight that performance increases with the size of training set, and that the best performance can only be achieved using the original resolution. In the reader study, performed on a random subset of the test set, we confirmed the efficacy of our model, which achieved performance comparable to a committee of radiologists when presented with the same data.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study (2018, background): “CNN architectures designed specifically to accommodate the higher resolution of radiological imaging have demonstrated promising early results [23,24].”
Transfer learning based histopathologic image classification for breast cancer detection (2018, methodology): “The multi task CNN was used to simultaneously predict malignant tumor and image magnification [10].”
Key challenges for delivering clinical impact with artificial intelligence (2019, background): “radiographs [5–9], detecting cancer in mammograms [10, 11], analysing computer tomography scans [12–15], identifying brain tumours on magnetic resonance images [16], and predicting development of Alzheimer’s disease from positron emission tomography [17].”
Disentangling Label Distribution for Long-tailed Visual Recognition (2020, background): “However, we argue that this evaluation protocol is often impractical as it is natural to assume that pt(y) could be the arbitrary distribution such as uniform distribution [50] and long-tailed distribution [17, 10].”
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening (2019, methodology): “For our model, we apply transfer learning from a network pretrained on a BI-RADS classification task, as in [14], which corresponds to predicting a radiologist’s assessment of a patient’s risk of having breast cancer based only on screening mammography.”