Xiaodong Guo

Research on Z boson decay patterns helps validate Standard Model predictions and explore new physical phenomena. However, analyzing large datasets from proton–proton colliders presents significant challenges, as traditional methods often struggle with efficiency and accuracy. To address this, we propose CNNBoosting, a novel ensemble learning algorithm that combines multiple weak convolutional neural networks (CNNs) to enhance classification performance. CNNBoosting targets two specific Z-boson decay modes (Zee and Zmumu), improving stability and accuracy in classifying particle interactions. Using a Z-boson dataset from Kaggle, we train and evaluate CNNBoosting, achieving impressive results: 92.74% accuracy, 92.74% precision, 92.78% recall and 92.75% F1 score. Comparisons with existing classifiers, including KNN, Random Forest, XGBoost, MLP, SVM,VGG-16, ResNet-50 and Decision Tree, demonstrate the superior performance of CNNBoosting. This is the first application of a CNN model to this dataset, and our results confirm its effectiveness in classifying Z-boson decay patterns, offering an efficient tool for researchers.

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, feature channels, and class activation maps, which caused a serious impact on the accuracy and interpretability of LSCC grading. While the traditional transformer block makes extensive use of parameter attention, the model overlearns the low-effect background semantic information, resulting in ineffectively reducing the proportion of background semantics. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), which improve the ability to learn high-effect target semantic information and reduce the proportion of background semantics. Firstly, according to generalized linear model and probabilistic, we demonstrate that learned-parameter-free attention (LA) has a stronger ability to learn highly effective target semantic information than parameter attention. Secondly, the first-type LA transformer block of LA-ViT utilizes the feature map position subspace to realize the query. Then, it uses the feature channel subspace to realize the key, and adopts the average convergence to obtain a value. And those construct the LA mechanism. Thus, it reduces the proportion of background semantics in the feature maps and feature channels. Thirdly, the second-type LA transformer block of LA-ViT uses the model probability matrix information and decision level weight information to realize key and query, respectively. And those realize the LA mechanism. So, it reduces the proportion of background semantics in class activation maps. Finally, we build a new complex semantic LSCC pathology image dataset to address the problem, which is less research on LSCC grading models because of lacking clinically meaningful datasets. After extensive experiments, the whole metrics of LA-ViT outperform those of other state-of-the-art methods, and the visualization maps match better with the regions of interest in the pathologists' decision-making. Moreover, the experimental results conducted on a public LSCC pathology image dataset show that LA-ViT has superior generalization performance to that of other state-of-the-art methods.

Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials. The limited number of photons within narrow energy bins leads to imaging results of low signal-noise ratio. The existing supervised deep reconstruction networks for CT reconstruction are difficult to address these challenges because it is usually impossible to acquire noise-free clinical images with clear structures as references. In this paper, we propose an iterative deep reconstruction network to synergize unsupervised method and data priors into a unified framework, named as Spectral2Spectral. Our Spectral2Spectral employs an unsupervised deep training strategy to obtain high-quality images from noisy data in an end-to-end fashion. The structural similarity prior within image-spectral domain is refined as a regularization term to further constrain the network training. The weights of neural network are automatically updated to capture image features and structures within the iterative process. Three large-scale preclinical datasets experiments demonstrate that the Spectral2spectral reconstructs better image quality than other the state-of-the-art methods.

Laryngeal Tumor Grading is a challenge task for computer-aided clinical diagnosis (CACD), mainly because the nuclei in histopathological images have large differences in shape and distribution, and the complex spatial relationship between nuclei. However, the existing CNN-based tumor grading models cannot adaptively represent the nuclei with variable morphology, and lack of effective modeling for the semantic information of nuclear spatial location. Therefore, we propose an end-to-end network (DCA-DAFFNet) with deformable convolution guided attention block (DCAB) and deep adaptive feature fusion (DAFF) to improve the grading ability. Specifically, a novel DCAB is designed to represent variable nuclei adaptively with more flexible receptive fields of deformable convolution. Besides, to solve the problem of poor semantic information modeling ability of nuclear spatial location, the vision transformer (ViT) block is incorporated into the attention-based CNN models to construct DCA-DAFFNet with powerful global information representation capability. Furthermore, a DAFF method containing multi-kernel max mean discrepancy (MK-MMD) and adaptive fusion block is proposed to solve the problem of negative fusion in DCA-DAFFNet. Extensive experiments results demonstrate the effectiveness of DCA-DAFFNet, and its average grading accuracy is increased to 90.78% ( ↑ 11.80%). By comparing visualization results, our proposed DCA-DAFFNet can pay more attention to the heterogeneous nuclei (polymorphic-nucleus, mega-nucleus, etc.) concerned by pathologists, which shows that our method more interpretable and human-computer interactive.

Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. Supervised deep denoising methods are popular but require paired clean or noisy samples that are often unavailable in practice. Limited by the independent noise assumption, current self-supervised denoising methods cannot process correlated noises as in CT images. Here we propose the first-of-its-kind similarity-based self-supervised deep denoising approach, referred to as Noise2Sim, that works in a nonlocal and nonlinear fashion to suppress not only independent but also correlated noises. Theoretically, Noise2Sim is asymptotically equivalent to supervised learning methods under mild conditions. Experimentally, Nosie2Sim recovers intrinsic features from noisy low-dose CT and photon-counting CT images as effectively as or even better than supervised learning methods on practical datasets visually, quantitatively and statistically. Noise2Sim is a general self-supervised denoising approach and has great potential in diverse applications.

Tomographic image reconstruction with deep learning is an emerging field of applied artificial intelligence. Reducing radiation dose with sparse views reconstruction is a significant task in cardiac imaging. Many efforts are contributing to sparse-view tomography imaging, but it is still a challenge for achieving good images from high sparse-view level, such as 60 views. In this study, we proposed a Deep Embedding-Attention-Refinement (DEAR) network to fundamentally address this challenge. DEAR consists of three modules including deep embedding, deep attention and deep refinement. The measurement is extended by deep embedding network to generate artifact-reduction images. Then, the deep attention network is employed to remove sparse-view artifacts and correct wrong details introduced by deep embedding network. Finally, the deep refinement module is used to refine finer image features and structures. The results on clinical datasets demonstrate the efficiency of our proposed DEAR in edge preservation and feature recovery.

In practice, cone-beam computed tomography (CT) images are usually tarnished by ring artifacts. To address this problem, in this article, we proposed a ring-artifact removal technique based on the nonlinear deviation correction of pixel response for flat panel detector (FPD), named Nonlinear Deviation CorrecTion (NDCT). Our proposed NDCT is a two-point fitting correction method of the local response from mean projection images. Its advantages lie in the following three aspects. First, it obtains correction coefficients by solving the correction equations based on the mean projection images without additional scanning, which improves efficiency. Second, it corrects all pixels of FPD at the same time, which eliminates the inconsistency of adjacent pixels and then the ring artifact within the 3-D CT image can be corrected. Third, the calculation is very simple so the correction speed is fast, which further improves efficiency in practice. Both numerical and real experiments showed that our NDCT method achieved excellent performance and fast speed in ring-artifact removal.

Wavelength-dependent absorption and scattering properties determine the fluorescence photon transport in biological tissues and image resolution of optical molecular tomography. Currently, these parameters are computed from optically measured data. For small animal imaging, estimation of optical parameters is a large-scale optimization problem, which is highly ill-posed. In this paper, we propose a new, to the best of our knowledge, approach to estimate optical parameters of biological tissues with photon-counting micro–computed tomography (micro-CT). From photon-counting x-ray data, multi-energy micro-CT images can be reconstructed to perform multi-organ segmentation and material decomposition in terms of tissue constituents. The concentration and characteristics of major tissue constituents can be utilized to calculate the optical absorption and scattering coefficients of the involved tissues. In our study, we perform numerical simulation, phantom experiments, and in vivo animal studies to calculate the optical parameters using our proposed approach. The results show that our approach can estimate optical parameters of tissues with a relative error of < 10 % , accurately mapping the optical parameter distributions in a small animal.

Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need for precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional methods use cross-entropy as the loss function with a Softmax classifier following a fully-connected layer. Most DL-based classification methods target intraclass relationships in a certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression, i.e., from an early stage and progress to a late stage. To learn both intraclass and interclass relationships among different stages and improve the accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB), and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In a 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional methods when we use modified ResNet-18 as the backbone. And precision, recall, and F 1-score are also improved. The experimental results show that our proposed method achieves a better classification performance than the traditional methods, which helps establish guidelines for the classification of COVID-19 chest CT images.