Objective: Ultrasound (US) examination has unique advantages in diagnosing
carpal tunnel syndrome (CTS) while identifying the median nerve (MN) and
diagnosing CTS depends heavily on the expertise of examiners. To alleviate this
problem, we aimed to develop a one-stop automated CTS diagnosis system
(OSA-CTSD) and evaluate its effectiveness as a computer-aided diagnostic tool.
Methods: We combined real-time MN delineation, accurate biometric measurements,
and explainable CTS diagnosis into a unified framework, called OSA-CTSD. We
collected a total of 32,301 static images from US videos of 90 normal wrists
and 40 CTS wrists for evaluation using a simplified scanning protocol. Results:
The proposed model showed better segmentation and measurement performance than
competing methods, reporting that HD95 score of 7.21px, ASSD score of 2.64px,
Dice score of 85.78%, and IoU score of 76.00%, respectively. In the reader
study, it demonstrated comparable performance with the average performance of
the experienced in classifying the CTS, while outperformed that of the
inexperienced radiologists in terms of classification metrics (e.g., accuracy
score of 3.59% higher and F1 score of 5.85% higher). Conclusion: The OSA-CTSD
demonstrated promising diagnostic performance with the advantages of real-time,
automation, and clinical interpretability. The application of such a tool can
not only reduce reliance on the expertise of examiners, but also can help to
promote the future standardization of the CTS diagnosis process, benefiting
both patients and radiologists.

通过开发和评估一种一站式自动化诊断系统（OSA-CTSD），本研究旨在缓解对检查员专业知识的依赖问题，通过实时中位神经勾画、准确生物测量和可解释的诊断集成框架，实现对腕管综合征（CTS）的一站式自动化诊断，展示了良好的分割和测量性能，并在读者研究中表现出与经验丰富的分类表现相媲美的性能，同时在分类指标方面优于经验不足的放射科医师，具有实时、自动化和临床可解释性等优点，既可以减少对检查员专业知识的依赖，也有助于促进 CTS 诊断过程的未来标准化，使得患者和放射科医师受益。

使用深度学习的超声图像自动化诊断系统一站式对腕管综合症的应用

One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in  Ultrasound Images Using Deep Learning

The chest X-ray plays a key role in screening and diagnosis of many lung
diseases including the COVID-19. More recently, many works construct deep
neural networks (DNNs) for chest X-ray images to realize automated and
efficient diagnosis of lung diseases. However, bias field caused by the
improper medical image acquisition process widely exists in the chest X-ray
images while the robustness of DNNs to the bias field is rarely explored, which
definitely poses a threat to the X-ray-based automated diagnosis system. In
this paper, we study this problem based on the recent adversarial attack and
propose a brand new attack, i.e., the adversarial bias field attack where the
bias field instead of the additive noise works as the adversarial perturbations
for fooling the DNNs. This novel attack posts a key problem: how to locally
tune the bias field to realize high attack success rate while maintaining its
spatial smoothness to guarantee high realisticity. These two goals contradict
each other and thus has made the attack significantly challenging. To overcome
this challenge, we propose the adversarial-smooth bias field attack that can
locally tune the bias field with joint smooth & adversarial constraints. As a
result, the adversarial X-ray images can not only fool the DNNs effectively but
also retain very high level of realisticity. We validate our method on real
chest X-ray datasets with powerful DNNs, e.g., ResNet50, DenseNet121, and
MobileNet, and show different properties to the state-of-the-art attacks in
both image realisticity and attack transferability. Our method reveals the
potential threat to the DNN-based X-ray automated diagnosis and can definitely
benefit the development of bias-field-robust automated diagnosis system.

本文提出了一种名为 “对抗性平滑偏置场攻击” 的新型对抗攻击方式，利用该攻击方式对胸部 X 射线图像进行处理能够有效欺骗深度神经网络（DNN）在自动化诊断肺部疾病方面的应用。在真实的胸部 X 射线数据集上，该攻击方式不仅在图像逼真度和攻击可转移三方面都表现出不同于现有研究的性质，而且还揭示了基于 DNN 的 X 射线自动诊断在偏置场情况下的潜在威胁，可以有助于开发偏置场鲁棒的自动化诊断系统。

偏差场对基于 DNN 的 X 射线识别构成威胁

Bias Field Poses a Threat to DNN-based X-Ray Recognition

In this study, we investigate what a practically useful approach is in order
to achieve robust skin disease diagnosis. A direct approach is to target the
ground truth diagnosis labels, while an alternative approach instead focuses on
determining skin lesion characteristics that are more visually consistent and
discernible. We argue that, for computer-aided skin disease diagnosis, it is
both more realistic and more useful that lesion type tags should be considered
as the target of an automated diagnosis system such that the system can first
achieve a high accuracy in describing skin lesions, and in turn facilitate
disease diagnosis using lesion characteristics in conjunction with other
evidence. To further meet such an objective, we employ convolutional neural
networks (CNNs) for both the disease-targeted and lesion-targeted
classifications. We have collected a large-scale and diverse dataset of 75,665
skin disease images from six publicly available dermatology atlantes. Then we
train and compare both disease-targeted and lesion-targeted classifiers,
respectively. For disease-targeted classification, only 27.6% top-1 accuracy
and 57.9% top-5 accuracy are achieved with a mean average precision (mAP) of
0.42. In contrast, for lesion-targeted classification, we can achieve a much
higher mAP of 0.70.

本文研究了实现强健的皮肤病诊断的实用方法，并提出一种以皮损为病变类型标签的自动化诊断系统作为目标的方法，并应用卷积神经网络（CNNs）来实现疾病定向诊断和病变定向分类的分类器，并采用来自六个公开皮肤病学大全的 75665 个皮肤病图像来进行训练与测试，结果证明基于病变定向分类的分类器的效果优于疾病定向的分类器。