Purpose: To analyze the demographic and imaging characteristics associated
with increased risk of failure for abnormality classification in screening
mammograms. Materials and Methods: This retrospective study used data from the
Emory BrEast Imaging Dataset (EMBED) which includes mammograms from 115,931
patients imaged at Emory University Healthcare between 2013 to 2020. Clinical
and imaging data includes Breast Imaging Reporting and Data System (BI-RADS)
assessment, region of interest coordinates for abnormalities, imaging features,
pathologic outcomes, and patient demographics. Multiple deep learning models
were developed to distinguish between patches of abnormal tissue and randomly
selected patches of normal tissue from the screening mammograms. We assessed
model performance overall and within subgroups defined by age, race, pathologic
outcome, and imaging characteristics to evaluate reasons for
misclassifications. Results: On a test set size of 5,810 studies (13,390
patches), a ResNet152V2 model trained to classify normal versus abnormal tissue
patches achieved an accuracy of 92.6% (95% CI = 92.0-93.2%), and area under the
receiver operative characteristics curve 0.975 (95% CI = 0.972-0.978). Imaging
characteristics associated with higher misclassifications of images include
higher tissue densities (risk ratio [RR]=1.649; p=.010, BI-RADS density C and
RR=2.026; p=.003, BI-RADS density D), and presence of architectural distortion
(RR=1.026; p<.001). Conclusion: Even though deep learning models for
abnormality classification can perform well in screening mammography, we
demonstrate certain imaging features that result in worse model performance.
This is the first such work to systematically evaluate breast abnormality
classification by various subgroups and better-informed developers and
end-users of population subgroups which are likely to experience biased model
performance.

通过分析筛选乳腺 X 线检查中失败的人群，使用深度学习模型识别正常和异常组织贴片，并评估人口子组的分类偏差，得出哪些影像特征是深度学习模型性能较差的原因。

人工智能模型乳腺 X 线筛查的性能差距 -- 迈向公平和可解释的模型

Performance Gaps of Artificial Intelligence Models Screening Mammography  -- Towards Fair and Interpretable Models

Breast cancer is a significant public health concern and early detection is
critical for triaging high risk patients. Sequential screening mammograms can
provide important spatiotemporal information about changes in breast tissue
over time. In this study, we propose a deep learning architecture called
RADIFUSION that utilizes sequential mammograms and incorporates a linear image
attention mechanism, radiomic features, a new gating mechanism to combine
different mammographic views, and bilateral asymmetry-based finetuning for
breast cancer risk assessment. We evaluate our model on a screening dataset
called Cohort of Screen-Aged Women (CSAW) dataset. Based on results obtained on
the independent testing set consisting of 1,749 women, our approach achieved
superior performance compared to other state-of-the-art models with area under
the receiver operating characteristic curves (AUCs) of 0.905, 0.872 and 0.866
in the three respective metrics of 1-year AUC, 2-year AUC and > 2-year AUC. Our
study highlights the importance of incorporating various deep learning
mechanisms, such as image attention, radiomic features, gating mechanism, and
bilateral asymmetry-based fine-tuning, to improve the accuracy of breast cancer
risk assessment. We also demonstrate that our model's performance was enhanced
by leveraging spatiotemporal information from sequential mammograms. Our
findings suggest that RADIFUSION can provide clinicians with a powerful tool
for breast cancer risk assessment.

通过序列乳腺摄影及深度学习方法，RADIFUSION 可以对乳腺癌患者的筛查进行风险评估，通过多样的深度学习机制和双侧不对称微调等方法来提高模型精度，并在 Cohort of Screen-Aged Women (CSAW) 数据集上显著提高了 1 年内、2 年内和 > 2 年的病人所受风险的区分度，是一种因素多样的乳腺癌风险评估工具。

RADIFUSION: 一种基于多放射组学、深度学习的乳腺癌风险预测模型，利用序列乳腺 X 射线照片、图像注意力和双侧不对称性通道的优化

RADIFUSION: A multi-radiomics deep learning based breast cancer risk  prediction model using sequential mammographic images with image attention  and bilateral asymmetry refinement