The absence of well-structured large datasets in medical computer vision
results in decreased performance of automated systems and, especially, of deep
learning models. Domain generalization techniques aim to approach unknown
domains from a single data source. In this paper we introduce a novel
framework, named CompStyle, which leverages style transfer and adversarial
training, along with high-level input complexity augmentation to effectively
expand the domain space and address unknown distributions. State-of-the-art
style transfer methods depend on the existence of subdomains within the source
dataset. However, this can lead to an inherent dataset bias in the image
creation. Input-level augmentation can provide a solution to this problem by
widening the domain space in the source dataset and boost performance on
out-of-domain distributions. We provide results from experiments on semantic
segmentation on prostate data and corruption robustness on cardiac data which
demonstrate the effectiveness of our approach. Our method increases performance
in both tasks, without added cost to training time or resources.

本文介绍了一种名为 CompStyle 的新型框架，利用样式转换和对抗训练以及高级输入复杂性增强来扩展域空间和处理未知的分布，以提高自动化系统和深度学习模型的性能。实验证明我们的方法在肾脏数据的语义分割和心脏数据的损坏鲁棒性上都能提高性能，而无需额外的培训时间或资源。

医学图像中的域泛化复杂风格转换

Complex Style Image Transformations for Domain Generalization in Medical  Images

Model initialization techniques are vital for improving the performance and
reliability of deep learning models in medical computer vision applications.
While much literature exists on non-medical images, the impacts on medical
images, particularly chest X-rays (CXRs) are less understood. Addressing this
gap, our study explores three deep model initialization techniques: Cold-start,
Warm-start, and Shrink and Perturb start, focusing on adult and pediatric
populations. We specifically focus on scenarios with periodically arriving data
for training, thereby embracing the real-world scenarios of ongoing data influx
and the need for model updates. We evaluate these models for generalizability
against external adult and pediatric CXR datasets. We also propose novel
ensemble methods: F-score-weighted Sequential Least-Squares Quadratic
Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy
Softmax to aggregate weight parameters from multiple models to capitalize on
their collective knowledge and complementary representations. We perform
statistical significance tests with 95% confidence intervals and p-values to
analyze model performance. Our evaluations indicate models initialized with
ImageNet-pre-trained weights demonstrate superior generalizability over
randomly initialized counterparts, contradicting some findings for non-medical
images. Notably, ImageNet-pretrained models exhibit consistent performance
during internal and external testing across different training scenarios.
Weight-level ensembles of these models show significantly higher recall
(p<0.05) during testing compared to individual models. Thus, our study
accentuates the benefits of ImageNet-pretrained weight initialization,
especially when used with weight-level ensembles, for creating robust and
generalizable deep learning solutions.

模型初始化技术在医学计算机视觉中的深度学习模型性能和可靠性的提高至关重要。本研究探讨了三种深度模型初始化技术：Cold-start、Warm-start 和 Shrink and Perturb start，并重点研究了成人和儿科人群的胸部 X 射线影像。我们还提出了新颖的集成方法：F-score-weighted Sequential Least-Squares Quadratic Programming（F-SLSQP）和 Attention-Guided Ensembles with Learnable Fuzzy Softmax，以整合来自多个模型的权重参数，并利用它们的集体知识和互补表示形式。研究结果表明，使用 ImageNet 预训练的权重初始化的模型在内部和外部测试中表现出卓越的泛化能力，与随机初始化的模型相比具有一致的性能，并且这些模型的权重级别集成在测试过程中具有更高的召回率 (p<0.05)。因此，本研究强调了使用 ImageNet 预训练权重初始化的好处，尤其是与权重级别的集成一起使用，以创建强大且具有广泛适用性的深度学习解决方案。

深度模型初始化对成人和儿科胸部 X 射线图像推广的影响揭示

Uncovering the effects of model initialization on deep model  generalization: A study with adult and pediatric Chest X-ray images

The discovery of adversarial examples has raised concerns about the practical
deployment of deep learning systems. In this paper, we demonstrate that
adversarial examples are capable of manipulating deep learning systems across
three clinical domains. For each of our representative medical deep learning
classifiers, both white and black box attacks were highly successful. Our
models are representative of the current state of the art in medical computer
vision and, in some cases, directly reflect architectures already seeing
deployment in real world clinical settings. In addition to the technical
contribution of our paper, we synthesize a large body of knowledge about the
healthcare system to argue that medicine may be uniquely susceptible to
adversarial attacks, both in terms of monetary incentives and technical
vulnerability. To this end, we outline the healthcare economy and the
incentives it creates for fraud and provide concrete examples of how and why
such attacks could be realistically carried out. We urge practitioners to be
aware of current vulnerabilities when deploying deep learning systems in
clinical settings, and encourage the machine learning community to further
investigate the domain-specific characteristics of medical learning systems.

本文发现对抗样本可以操纵深度学习系统在三个临床领域，建议医疗机构在部署深度学习系统时注意当前的漏洞，同时促进机器学习社区进一步研究医疗学习系统的领域特征。