The novel 2019 Coronavirus disease (COVID-19) global pandemic is a defining
health crisis. Recent efforts have been increasingly directed towards achieving
quick and accurate detection of COVID-19 across symptomatic patients to
mitigate the intensity and spread of the disease. Artificial intelligence (AI)
algorithms applied to chest X-ray (CXR) images have emerged as promising
diagnostic tools, and previous work has demonstrated impressive classification
performances. However, such methods have faced criticisms from physicians due
to their black-box reasoning process and unpredictable nature. In contrast to
professional radiologist diagnosis, AI systems often lack generalizability,
explainability, and robustness in the clinical decision making process. In our
work, we address these issues by first proposing an extensive baseline study,
training and evaluating 21 convolutional neural network (CNN) models on a
diverse set of 33,000+ CXR images to classify between healthy, COVID-19, and
non-COVID-19 pneumonia CXRs. Our resulting models achieved a 3-way
classification accuracy, recall, and precision of up to 97.03\%, 97.97\%, and
99.95\%, respectively. Next, we investigate the effectiveness of adversarial
training on model robustness and explainability via Gradient-weighted Class
Activation Mapping (Grad-CAM) heatmaps. We find that adversarially trained
models not only significantly outperform their standard counterparts on
classifying perturbed images, but also yield saliency maps that 1) better
specify clinically relevant features, 2) are robust against extraneous
artifacts, and 3) agree considerably more with expert radiologist findings.

通过 21 个卷积神经网络模型在多样化的 33,000 + 胸部 X 射线图像上进行训练和评估，我们的研究旨在解决人工智能在 COVID-19 检测中的可解释性和鲁棒性问题，通过对抗训练提高了模型的鲁棒性，并生成了与专业放射科医生发现相一致的突出热力图。

基于对抗训练的鲁棒可解释性胸部 X 射线 COVID-19 诊断

Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images using  Adversarial Training

Deep neural networks (DNNs) are well known to be vulnerable to adversarial
examples (AEs). In addition, AEs have adversarial transferability, which means
AEs generated for a source model can fool another black-box model (target
model) with a non-trivial probability. In previous studies, it was confirmed
that the vision transformer (ViT) is more robust against the property of
adversarial transferability than convolutional neural network (CNN) models such
as ConvMixer, and moreover encrypted ViT is more robust than ViT without any
encryption. In this article, we propose a random ensemble of encrypted ViT
models to achieve much more robust models. In experiments, the proposed scheme
is verified to be more robust against not only black-box attacks but also
white-box ones than convention methods.

本文中，我们提出了一种随机集成的加密视觉转换模型，以实现更加强健的模型，这种模型不仅对黑盒攻击更加强健，而且对白盒攻击也更加强健。

使用加密视觉转换器模型的随机集合增强对抗样本的安全性

Enhanced Security against Adversarial Examples Using a Random Ensemble  of Encrypted Vision Transformer Models

Flexible road pavements deteriorate primarily due to traffic and adverse
environmental conditions. Cracking is the most common deterioration mechanism;
the surveying thereof is typically conducted manually using internationally
defined classification standards. In South Africa, the use of high-definition
video images has been introduced, which allows for safer road surveying.
However, surveying is still a tedious manual process. Automation of the
detection of defects such as cracks would allow for faster analysis of road
networks and potentially reduce human bias and error. This study performs a
comparison of six state-of-the-art convolutional neural network models for the
purpose of crack detection. The models are pretrained on the ImageNet dataset,
and fine-tuned using a new real-world binary crack dataset consisting of 14000
samples. The effects of dataset augmentation are also investigated. Of the six
models trained, five achieved accuracy above 97%. The highest recorded accuracy
was 98%, achieved by the ResNet and VGG16 models. The dataset is available at
the following URL: this https URL

该研究使用了六种卷积神经网络模型来进行道路裂缝的检测，并使用一个由 14000 个样本组成的新型真实二元裂缝数据集进行微调，观察数据集扩增的效果，并得出 ResNet 和 VGG16 模型具有最高精度为 98% 的结论。

用于柔性路面裂缝检测的卷积神经网络

Convolutional neural networks for crack detection on flexible road  pavements

The increasingly common use of incidental satellite images for stereo
reconstruction versus rigidly tasked binocular or trinocular coincident
collection is helping to enable timely global-scale 3D mapping; however,
reliable stereo correspondence from multi-date image pairs remains very
challenging due to seasonal appearance differences and scene change. Promising
recent work suggests that semantic scene segmentation can provide a robust
regularizing prior for resolving ambiguities in stereo correspondence and
reconstruction problems. To enable research for pairwise semantic stereo and
multi-view semantic 3D reconstruction with incidental satellite images, we have
established a large-scale public dataset including multi-view, multi-band
satellite images and ground truth geometric and semantic labels for two large
cities. To demonstrate the complementary nature of the stereo and segmentation
tasks, we present lightweight public baselines adapted from recent state of the
art convolutional neural network models and assess their performance.

通过语义场景分割来提供强大的正则化先验以解决立体对应和重建问题的不确定性，利用无意卫星图像进行成对语义立体和多视角语义三维重建的研究已经成为可能。