This work proposes a framework developed to generalize Critical Heat Flux (CHF) detection classification models using an Unsupervised Image-to-Image (UI2I) translation model. The framework enables a typical classification model that was trained and tested on boiling images from domain A to predict boiling images coming from domain B that was never seen by the classification model. This is done by using the UI2I model to transform the domain B images to look like domain A images that the classification model is familiar with. Although CNN was used as the classification model and Fixed-Point GAN (FP-GAN) was used as the UI2I model, the framework is model agnostic. Meaning, that the framework can generalize any image classification model type, making it applicable to a variety of similar applications and not limited to the boiling crisis detection problem. It also means that the more the UI2I models advance, the better the performance of the framework.

头颈肿瘤分割挑战（Hecktor）2022为研究人员提供了一个平台，可以将其解决方案与3D CT和PET图像的肿瘤和淋巴结分割。在这项工作中，我们描述了针对Hecktor 2022分割任务的解决方案。我们将所有图像重新样本为共同的分辨率，在头颈部和颈部区域周围的作物，并从Monai训练Segresnet语义分割网络。我们使用5倍的交叉验证来选择最佳模型检查点。最终提交是3次运行中的15个型号的合奏。我们的解决方案（NVAUTO团队名称）以0.78802的汇总骰子得分在Hecktor22挑战排行榜上获得第一名。

颅内出血分割挑战（实例2022）为研究人员提供了一个平台，以将其解决方案与3D CTS的出血中风区域进行分割。在这项工作中，我们将解决方案描述为实例2022。我们使用2D分割网络，来自Monai的Segresnet，在不重采样的情况下操作切片。最终提交是18个模型的合奏。我们的解决方案（NVAUTO团队名称）在骰子度量标准（0.721）和总排名2方面获得了最高位置。

从MRI和X射线等医学图像中自动检测的自动异常可显着减少人类在疾病诊断方面的努力。由于建模异常的复杂性以及领域专家（例如放射科医生）的高度手动注释成本，因此当前医学成像文献中的典型技术仅着重于从健康对象中得出诊断模型，假设该模型将检测到图像，来自患者作为异常值。但是，在许多实际情况下，与健康和患病患者混合在一起的未注释的数据集很丰富。因此，本文提出了一个研究问题，即如何通过（1）（1）（1）（2）（2）文献中使用的一组健康图像来改善无监督的异常检测。为了回答这个问题，我们提出了一种新型的单向图像到图像翻译方法的Healthygan，该方法学会了将图像从混合数据集中转换为仅健康图像。作为一方面的Healthygan，Healthygan放宽了现有未配对的图像到图像翻译方法的循环一致性的要求，这对于混合的未注释数据是无法实现的。一旦学习了翻译，我们通过减去其翻译输出来为任何给定图像生成差异图。差异图中显着响应的区域对应于潜在异常（如果有）。我们的Healthygan在两个公开可用的数据集上优于传统的最先进方法：Covid-19和NIH Chestx-Ray14，以及从Mayo Clinic收集的一个机构数据集。该实施可在https://github.com/mahfuzmohammad/healthygan上公开获得。

由于物体形状和图案（例如器官或肿瘤）的高可变性，3D医学图像的语义分割是一个具有挑战性的任务。鉴于最近在医学图像分割中深入学习的成功，已经引入了神经结构搜索（NAS）以查找高性能3D分段网络架构。但是，由于3D数据的大量计算要求和架构搜索的离散优化性质，之前的NAS方法需要很长的搜索时间或必要的连续放松，并且通常导致次优网络架构。虽然单次NAS可能会解决这些缺点，但其在分段域中的应用尚未在膨胀的多尺度多路径搜索空间中进行很好地研究。为了为医学图像分割启用一次性NAS，我们的方法名为Hypersegnas，介绍了通过结合建筑拓扑信息来帮助超级培训培训。在培训超级网络培训并在架构搜索期间引入开销时，可以删除这种超空头。我们表明，与以前的最先进的（SOTA）分割网络相比，Hypersegnas产生更好的表现和更直观的架构;此外，它可以在不同的计算限制下快速准确地找到良好的体系结构候选者。我们的方法是在医疗细分Decovaton（MSD）挑战的公共数据集上评估，并实现了SOTA表演。

多模式脑肿瘤分割挑战（BRALS）2021的另一年提供了较大的数据集，以促进脑肿瘤分割方法的合作和研究，这对于疾病分析和治疗规划是必要的。 BRATS 2021的大型数据集大小和现代GPU的出现为学习基于深度学习的方法提供了更好的机会，以学习来自数据的肿瘤表示。在这项工作中，我们维护了一个基于编码器解码器的分段网络，但专注于网络培训过程的修改，从而最大限度地减少扰动下的冗余。鉴于培训的网络，我们进一步介绍了基于置信的组合技术，以进一步提高性能。我们评估了Brats 2021验证板上的方法，并分别为增强肿瘤核心，肿瘤核心和全肿瘤的0.8600,0.8868和0.9265平均骰子。我们的团队（NVAUTO）提交是在ET和TC分数方面的最高表演，并且在WT分数方面的十大表演团队内。

Unsupervised learning-based anomaly detection in latent space has gained importance since discriminating anomalies from normal data becomes difficult in high-dimensional space. Both density estimation and distance-based methods to detect anomalies in latent space have been explored in the past. These methods prove that retaining valuable properties of input data in latent space helps in the better reconstruction of test data. Moreover, real-world sensor data is skewed and non-Gaussian in nature, making mean-based estimators unreliable for skewed data. Again, anomaly detection methods based on reconstruction error rely on Euclidean distance, which does not consider useful correlation information in the feature space and also fails to accurately reconstruct the data when it deviates from the training distribution. In this work, we address the limitations of reconstruction error-based autoencoders and propose a kernelized autoencoder that leverages a robust form of Mahalanobis distance (MD) to measure latent dimension correlation to effectively detect both near and far anomalies. This hybrid loss is aided by the principle of maximizing the mutual information gain between the latent dimension and the high-dimensional prior data space by maximizing the entropy of the latent space while preserving useful correlation information of the original data in the low-dimensional latent space. The multi-objective function has two goals -- it measures correlation information in the latent feature space in the form of robust MD distance and simultaneously tries to preserve useful correlation information from the original data space in the latent space by maximizing mutual information between the prior and latent space.

The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. One challenge the development of IoT faces is the existence of anomaly data in the network. Therefore, research on anomaly detection in the IoT environment has become popular and necessary in recent years. This survey provides an overview to understand the current progress of the different anomaly detection algorithms and how they can be applied in the context of the Internet of Things. In this survey, we categorize the widely used anomaly detection machine learning and deep learning techniques in IoT into three types: clustering-based, classification-based, and deep learning based. For each category, we introduce some state-of-the-art anomaly detection methods and evaluate the advantages and limitations of each technique.

Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people, especially in developing and impoverished countries where high levels of pollution, unclean living conditions, and overcrowding are frequently observed, along with insufficient medical infrastructure. Pleural effusion, a condition in which fluids fill the lung and complicate breathing, is brought on by pneumonia. Early detection of pneumonia is essential for ensuring curative care and boosting survival rates. The approach most usually used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is to develop a method for the automatic diagnosis of bacterial and viral pneumonia in digital x-ray pictures. This article first presents the authors' technique, and then gives a comprehensive report on recent developments in the field of reliable diagnosis of pneumonia. In this study, here tuned a state-of-the-art deep convolutional neural network to classify plant diseases based on images and tested its performance. Deep learning architecture is compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152, Mobilenettv2, and DenseNet with 201 layers are among the architectures tested. Experiment data consists of two groups, sick and healthy X-ray pictures. To take appropriate action against plant diseases as soon as possible, rapid disease identification models are preferred. DenseNet201 has shown no overfitting or performance degradation in our experiments, and its accuracy tends to increase as the number of epochs increases. Further, DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time. This architecture outperforms the competition in terms of testing accuracy, scoring 95%. Each architecture was trained using Keras, using Theano as the backend.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.