The future of population-based breast cancer screening is likely personalized strategies based on clinically relevant risk models. Mammography-based risk models should remain robust to domain shifts caused by different populations and mammographic devices. Modern risk models do not ensure adaptation across vendor-domains and are often conflated to unintentionally rely on both precursors of cancer and systemic/global mammographic information associated with short- and long-term risk, respectively, which might limit performance. We developed a robust, cross-vendor model for long-term risk assessment. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization to an unseen vendor-domain. We trained on samples without diagnosed/potential malignant findings to learn systemic/global breast tissue features, called mammographic texture, indicative of future breast cancer. However, training so may cause erratic convergence. By excluding noise-inducing samples and designing a case-control dataset, a robust ensemble texture model was trained. This model was validated in two independent datasets. In 66,607 Danish women with flavorized Siemens views, the AUC was 0.71 and 0.65 for prediction of interval cancers within two years (ICs) and from two years after screening (LTCs), respectively. In a combination with established risk factors, the model's AUC increased to 0.68 for LTCs. In 25,706 Dutch women with Hologic-processed views, the AUCs were not different from the AUCs in Danish women with flavorized views. The results suggested that the model robustly estimated long-term risk while adapting to an unseen processed vendor-domain. The model identified 8.1% of Danish women accounting for 20.9% of ICs and 14.2% of LTCs.

基于深入的学习的诊断性能随着更多的注释数据而增加，但手动注释是大多数领域的瓶颈。专家在临床常规期间评估诊断图像，并在报告中写出他们的调查结果。基于临床报告的自动注释可以克服手动标记瓶颈。我们假设可以使用这些报告的稀疏信息引导的模型预测来生成用于检测任务的密度注释。为了证明疗效，我们在放射学报告中临床显着发现的数量指导的临床上显着的前列腺癌（CSPCA）注释。我们包括7,756个前列腺MRI检查，其中3,050人被手动注释，4,706次自动注释。我们对手动注释的子集进行了自动注释质量：我们的得分提取正确地确定了99.3 \％$ 99.3 \％$ 99.3 \％$的CSPCA病变数量，我们的CSPCA分段模型正确地本地化了83.8 \ PM 1.1 \％$的病变。我们评估了来自外部中心的300名检查前列腺癌检测表现，具有组织病理学证实的基础事实。通过自动标记的考试增强培训集改善了在接收器的患者的诊断区域，从$ 88.1 \ pm 1.1 \％$至89.8 \ pm 1.0 \％$（$ p = 1.2 \ cdot 10 ^ { - 4} $ ）每案中的一个错误阳性的基于病变的敏感性，每案件从79.2美元2.8 \％$ 85.4 \ PM 1.9 \％$（$ P <10 ^ { - 4} $），以$ alm \ pm std。$超过15个独立运行。这种改进的性能展示了我们报告引导的自动注释的可行性。源代码在https://github.com/diagnijmegen/report-guiding-annotation上公开可用。最佳的CSPCA检测算法在https://grand-challenge.org/algorithms/bpmri-cspca-detection-report-guiding-annotations/中提供。

不观察到的混淆是估算因果效应时的主要挑战之一。我们提出了一种因果还原方法，给出因果模型，用一个潜在的混淆器取代了一个任意数量的可能的高维潜在混淆，这些混淆是在同一空间中的价值观，而不改变原因的观察和介入分布模特需要。这使我们能够以合并数据的原则方式估计因果效应而不依赖于普遍但往往不切实际的假设，即所有的混乱。我们在三种不同的设置中应用了我们的因果化。在第一个设置中，我们假设治疗和结果是离散的。随后的因果还原暗示可以利用估计目的的观察和介入分布之间的界限。在某些情况下具有高度不平衡的观察样本的情况下，通过掺入观察数据，可以提高因果效应估计的准确性。其次，对于连续变量并假设线性高斯模型，我们导出了对观察和介入分布的参数的平等约束。第三，对于一般连续设置（可能是非线性或非高斯），我们使用标准化流量参数化减少的因果模型，灵活的易于可逆的非线性变换。我们对合成数据进行一系列实验，发现在几个情况下，在不牺牲精度的情况下添加观察训练样本时，可以减少介入样本的数量。