Purpose: Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI‐RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI‐RADS categories, i.e., “scattered density” and “heterogeneously dense”. The aim of this work was to investigate a deep learning‐based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI‐RADS category in current clinical workflow.Methods: In this study, we constructed a convolutional neural network (CNN)‐based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board‐certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier.Results: The AUC was 0.9421 when the CNN‐model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine‐tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset.Conclusions: Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.

Purpose: To investigate two deep learning‐based modeling schemes for predicting short‐term risk of developing breast cancer using prior normal screening digital mammograms in a case‐control setting.Methods: We conducted a retrospective Institutional Review Board‐approved study on a case‐control cohort of 226 patients (including 113 women diagnosed with breast cancer and 113 controls) who underwent general population breast cancer screening. For each patient, a prior normal (i.e., with negative or benign findings) digital mammogram examination [including mediolateral oblique (MLO) view and craniocaudal (CC) view two images] was collected. Thus, a total of 452 normal images (226 MLO view images and 226 CC view images) of this case‐control cohort were analyzed to predict the outcome, i.e., developing breast cancer (cancer cases) or remaining breast cancer‐free (controls) within the follow‐up period. We implemented an end‐to‐end deep learning model and a GoogLeNet‐LDA model and compared their effects in several experimental settings using two mammographic view images and inputting two different subregions of the images to the models. The proposed models were also compared to logistic regression modeling of mammographic breast density. Area under the receiver operating characteristic curve (AUC) was used as the model performance metric.Results: The highest AUC was 0.73 [95% Confidence Interval (CI): 0.68–0.78; GoogLeNet‐LDA model on CC view] when using the whole‐breast and was 0.72 (95% CI: 0.67–0.76; GoogLeNet‐LDA model on MLO + CC view) when using the dense tissue, respectively, as the model input. The GoogleNet‐LDA model significantly (all P < 0.05) outperformed the end‐to‐end GoogLeNet model in all experiments. CC view was consistently more predictive than MLO view in both deep learning models, regardless of the input subregions. Both models exhibited superior performance than the percent breast density (AUC = 0.54; 95% CI: 0.49–0.59).Conclusions: The proposed deep learning modeling approach can predict short‐term breast cancer risk using normal screening mammogram images. Larger studies are needed to further reveal the promise of deep learning in enhancing imaging‐based breast cancer risk assessment.

Conventional federated learning (FL) trains one global model for a federation of clients with decentralized data, reducing the privacy risk of centralized training. However, the distribution shift across non-IID datasets, often poses a challenge to this one-model-fits-all solution. Personalized FL aims to mitigate this issue systematically. In this work, we propose APPLE, a personalized cross-silo FL framework that adaptively learns how much each client can benefit from other clients’ models. We also introduce a method to flexibly control the focus of training APPLE between global and local objectives. We empirically evaluate our method's convergence and generalization behaviors, and perform extensive experiments on two benchmark datasets and two medical imaging datasets under two non-IID settings. The results show that the proposed personalized FL framework, APPLE, achieves state-of-the-art performance compared to several other personalized FL approaches in the literature. The code is publicly available at https://github.com/ljaiverson/pFL-APPLE.

Purpose: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish recalled but benign mammography images from negative exams and those with malignancy. Experimental Design: Deep learning convolutional neural network (CNN) models were constructed to classify mammography images into malignant (breast cancer), negative (breast cancer free), and recalled-benign categories. A total of 14,860 images of 3,715 patients from two independent mammography datasets: Full-Field Digital Mammography Dataset (FFDM) and a digitized film dataset, Digital Dataset of Screening Mammography (DDSM), were used in various settings for training and testing the CNN models. The ROC curve was generated and the AUC was calculated as a metric of the classification accuracy. Results: Training and testing using only the FFDM dataset resulted in AUC ranging from 0.70 to 0.81. When the DDSM dataset was used, AUC ranged from 0.77 to 0.96. When datasets were combined for training and testing, AUC ranged from 0.76 to 0.91. When pretrained on a large nonmedical dataset and DDSM, the models showed consistent improvements in AUC ranging from 0.02 to 0.05 (all P > 0.05), compared with pretraining only on the nonmedical dataset. Conclusions: This study demonstrates that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish recalled-benign images from malignant and negative cases, which may lead to a computerized clinical toolkit to help reduce false recalls. Clin Cancer Res; 1–8. ©2018 AACR.