A Modified CycleGAN for Multi-Organ Ultrasound Image Enhancement via Unpaired Pre-Training

Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by doctors.At the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated.Therefore,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training.We introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired datasets.We also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training phases.To improve the robustness of the model,we add Gaussian noise to the training images as data augmentation.Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.

Deep Stacked Ensemble Learning Model for COVID-19 Classification

COVID-19 is a growing problem worldwide with a high mortality rate.As a result,the World Health Organization(WHO)declared it a pandemic.In order to limit the spread of the disease,a fast and accurate diagnosis is required.A reverse transcript polymerase chain reaction(RT-PCR)test is often used to detect the disease.However,since this test is time-consuming,a chest computed tomography(CT)or plain chest X-ray(CXR)is sometimes indicated.The value of automated diagnosis is that it saves time and money by minimizing human effort.Three significant contributions are made by our research.Its initial purpose is to use the essential finetuning methodology to test the action and efficiency of a variety of vision models,ranging from Inception to Neural Architecture Search(NAS)networks.Second,by plotting class activationmaps(CAMs)for individual networks and assessing classification efficiency with AUC-ROC curves,the behavior of these models is visually analyzed.Finally,stacked ensembles techniques were used to provide greater generalization by combining finetuned models with six ensemble neural networks.Using stacked ensembles,the generalization of the models improved.Furthermore,the ensemble model created by combining all of the finetuned networks obtained a state-of-the-art COVID-19 accuracy detection score of 99.17%.The precision and recall rates were 99.99%and 89.79%,respectively,highlighting the robustness of stacked ensembles.The proposed ensemble approach performed well in the classification of the COVID-19 lesions on CXR according to the experimental results.

迁移学习在热轧钢带表面缺陷分类中应用研究

为了提高热轧钢带表面缺陷分类的检测准确率和速度,同时鉴于热轧钢带缺陷的数据库规模较小,提出结合参数迁移学习的卷积神经网络模型,来解决少量样本导致网络过拟合和精度低的问题。使用源域的最优参数作为模型的参数初始化,节省训练的周期;构建训练目标域的神经网络模型,使用预训练模型网络中的参数和结构,对目标域进行特征迁移;进行finetune,结合inception-v3结构的全连接层映射到目标域所需要的特征向量维度。实验使用现有热轧钢带表面缺陷数据库中的图片,有6类缺陷。通过对比改进AlexNet模型和结合迁移学习的模型,在测试集的实验平均准确率分别约为96.6%,99.8%,分类效果优于传统视觉分类算法。并且在实验中观察到结合参数迁移学习的损失更小和权重收敛速度更快。

Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments

The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural scenarios.However,a conspicuous gap endures in the literature concerning the training and testing of models across disparate environmental conditions.Predominant methodologies either delineate a single dataset distribution into training,validation,and testing subsets or merge datasets from diverse condi-tions or distributions before their division into the subsets.Our study aims to ameliorate this gap by extending to several broadleaf weed categories across varied distributions,evaluating the impact of training convolutional neural networks on datasets specific to particular conditions or distributions,and assessing their performance in entirely distinct settings through three experiments.By evaluating diverse network architectures and training approaches(finetuning versus feature extraction),testing various architectures,employing different training strategies,and amalgamating data,we devised straightforward guidelines to ensure the model's deployability in contrasting environments with sustained precision and accuracy.In Experiment 1,conducted in a uniform environment,accuracy ranged from 80%to 100%across all models and training strategies,with finetune mode achieving a superior performance of 94%to 99.9%compared to the feature extraction mode at 80%to 92.96%.Experiment 2 underscored a significant performance decline,with accuracy fig-ures between 25%and 60%,primarily at 40%,when the origin of the test data deviated from the train and valida-tion sets.Experiment 3,spotlighting dataset and distribution amalgamation,yielded promising accuracy metrics,notably a peak of 99.6%for ResNet in finetuning mode to a low of 69.9%for InceptionV3 in feature extraction mode.These pivotal findings emphasize that merging data from diverse distributions,coupled with finetuned training on advanced architectures like ResNet and MobileNet,markedly enhances performance,contrasting with the rel-atively lower performance exhibited by simpler networks like AlexNet.Our results suggest that embracing data diversity and flexible training methodologies are crucial for optimizing weed classification models when dispa-rate data distributions are available.This study gives a practical alternative for treating diverse datasets with real-world agricultural variances.

Single Image Deraining Using Residual Channel Attention Networks

Image deraining is a highly ill-posed problem.Although significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and generalization to real rain images.In this paper,we propose a deep residual channel attention network(DeRCAN)for deraining.The channel attention mechanism is able to capture the inherent properties of the feature space and thus facilitates more accurate estimations of structures and details for image deraining.In addition,we further propose an unsupervised learning approach to better solve real rain images based on the proposed network.Extensive qualitative and quantitative evaluation results on both synthetic and real-world images demonstrate that the proposed DeRCAN performs favorably against state-of-the-art methods.