Modeling refined differences of cortical folding patterns via spatial, morphological, and temporal fusion representations.

The gyrus, a pivotal cortical folding pattern, is essential for integrating brain structure-function. This study focuses on 2-Hinge and 3-Hinge folds, characterized by the gyral convergence from various directions. Existing voxel-level studies may not adequately capture the precise spatial relationships within cortical folding patterns, especially when relying solely on local cortical characteristics due to their variable shapes and homogeneous frequency-specific features. To overcome these challenges, we introduced a novel model that combines spatial distribution, morphological structure, and functional magnetic resonance imaging data. We utilized spatio-morphological residual representations to enhance and extract subtle variations in cortical spatial distribution and morphological structure during blood oxygenation, integrating these with functional magnetic resonance imaging embeddings using self-attention for spatio-morphological-temporal representations. Testing these representations for identifying cortical folding patterns, including sulci, gyri, 2-Hinge, and 2-Hinge folds, and evaluating the impact of phenotypic data (e.g. stimulus) on recognition, our experimental results demonstrate the model's superior performance, revealing significant differences in cortical folding patterns under various stimulus. These differences are also evident in the characteristics of sulci and gyri folds between genders, with 3-Hinge showing more variations. Our findings indicate that our representations of cortical folding patterns could serve as biomarkers for understanding brain structure-function correlations.

Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images.

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal's, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance.

LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting.

BACKGROUND: A large number of experimental studies show that the mutation and regulation of long non-coding RNAs (lncRNAs) are associated with various human diseases. Accurate prediction of lncRNA-disease associations can provide a new perspective for the diagnosis and treatment of diseases. The main function of many lncRNAs is still unclear and using traditional experiments to detect lncRNA-disease associations is time-consuming. RESULTS: In this paper, we develop a novel and effective method for the prediction of lncRNA-disease associations using network feature similarity and gradient boosting (LDNFSGB). In LDNFSGB, we first construct a comprehensive feature vector to effectively extract the global and local information of lncRNAs and diseases through considering the disease semantic similarity (DISSS), the lncRNA function similarity (LNCFS), the lncRNA Gaussian interaction profile kernel similarity (LNCGS), the disease Gaussian interaction profile kernel similarity (DISGS), and the lncRNA-disease interaction (LNCDIS). Particularly, two methods are used to calculate the DISSS (LNCFS) for considering the local and global information of disease semantics (lncRNA functions) respectively. An autoencoder is then used to reduce the dimensionality of the feature vector to obtain the optimal feature parameter from the original feature set. Furthermore, we employ the gradient boosting algorithm to obtain the lncRNA-disease association prediction. CONCLUSIONS: In this study, hold-out, leave-one-out cross-validation, and ten-fold cross-validation methods are implemented on three publicly available datasets to evaluate the performance of LDNFSGB. Extensive experiments show that LDNFSGB dramatically outperforms other state-of-the-art methods. The case studies on six diseases, including cancers and non-cancers, further demonstrate the effectiveness of our method in real-world applications.

Hierarchical neural architecture search with adaptive global-local feature learning for Magnetic Resonance Image reconstruction.

Neural architecture search (NAS) has been introduced into the design of deep neural network architectures for Magnetic Resonance Imaging (MRI) reconstruction since NAS-based methods can acquire the complex network architecture automatically without professional designing experience and improve the model's generalization ability. However, current NAS-based MRI reconstruction methods suffer from a lack of efficient operators in the search space, which leads to challenges in effectively recovering high-frequency details. This limitation is primarily due to the prevalent use of convolution operators in the current search space, which struggle to capture both global and local features of MR images simultaneously, resulting in insufficient information utilization. To address this issue, a generative adversarial network (GAN) based model is proposed to reconstruct the MR image from under-sampled K-space data. Firstly, parameterized global and local feature learning modules at multiple scales are added into the search space to improve the capability of recovering high-frequency details. Secondly, to mitigate the increased search time caused by the augmented search space, a hierarchical NAS is designed to learn the global-local feature learning modules that enable the reconstruction network to learn global and local information of MR images at different scales adaptively. Thirdly, to reduce the number of network parameters and computational complexity, the standard operations in global-local feature learning modules are replaced with lightweight operations. Finally, experiments on several publicly available brain MRI image datasets evaluate the performance of the proposed method. Compared to the state-of-the-art MRI reconstruction methods, the proposed method yields better reconstruction results in terms of peak signal-to-noise ratio and structural similarity at a lower computational cost. Additionally, our reconstruction results are validated through a brain tumor classification task, affirming the practicability of the proposed method. Our code is available at https://github.com/wwHwo/HNASMRI.

ADHD diagnosis guided by functional brain networks combined with domain knowledge.

Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks (FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research, revealing neural impact and mechanisms through the exploration of activated brain regions. However, current FBNs-based methods face two major challenges. The primary challenge stems from the limitations of existing modeling methods in accurately capturing both regional correlations and long-distance dependencies (LDDs) within the dynamic brain, thereby affecting the diagnostic accuracy of FBNs as biomarkers. Additionally, limited sample size and class imbalance also pose a challenge to the learning performance of the model. To address the issues, we propose an automated diagnostic framework, which integrates modeling, multimodal fusion, and classification into a unified process. It aims to extract representative FBNs and efficiently incorporate domain knowledge to guide ADHD classification. Our work mainly includes three-fold: 1) A multi-head attention-based region-enhancement module (MAREM) is designed to simultaneously capture regional correlations and LDDs across the entire sequence of brain activity, which facilitates the construction of representative FBNs. 2) The multimodal supplementary learning module (MSLM) is proposed to integrate domain knowledge from phenotype data with FBNs from neuroimaging data, achieving information complementarity and alleviating the problems of insufficient medical data and unbalanced sample categories. 3) An ADHD automatic diagnosis framework guided by FBNs and domain knowledge (ADF-FAD) is proposed to help doctors make more accurate decisions, which is applied to the ADHD-200 dataset to confirm its effectiveness. The results indicate that the FBNs extracted by MAREM perform well in modeling and classification. After with MSLM, the model achieves accuracy of 92.4%, 74.4%, and 80% at NYU, PU, and KKI, respectively, demonstrating its ability to effectively capture crucial information related to ADHD diagnosis. Codes are available at https://github.com/zhuimengxuebao/ADF-FAD.

MCNet: A multi-level context-aware network for the segmentation of adrenal gland in CT images.

Accurate segmentation of the adrenal gland from abdominal computed tomography (CT) scans is a crucial step towards facilitating the computer-aided diagnosis of adrenal-related diseases such as essential hypertension and adrenal tumors. However, the small size of the adrenal gland, which occupies less than 1% of the abdominal CT slice, poses a significant challenge to accurate segmentation. To address this problem, we propose a novel multi-level context-aware network (MCNet) to segment adrenal glands in CT images. Our MCNet mainly consists of two components, i.e., the multi-level context aggregation (MCA) module and multi-level context guidance (MCG) module. Specifically, the MCA module employs multi-branch dilated convolutional layers to capture geometric information, which enables handling of changes in complex scenarios such as variations in the size and shape of objects. The MCG module, on the other hand, gathers valuable features from the shallow layer and leverages the complete utilization of feature information at different resolutions in various codec stages. Finally, we evaluate the performance of the MCNet on two CT datasets, including our clinical dataset (Ad-Seg) and a publicly available dataset known as Distorted Golden Standards (DGS), from different perspectives. Compared to ten other state-of-the-art segmentation methods, our MCNet achieves 71.34% and 75.29% of the best Dice similarity coefficient on the two datasets, respectively, which is at least 2.46% and 1.19% higher than other segmentation methods.

BIRGU Net: deformable brain magnetic resonance image registration using gyral-net map and 3D Res-Unet.

Deformable image registration is a fundamental procedure in medical imaging. Recently, deep learning-based deformable image registrations have achieved fast registration by learning the spatial correspondence from image pairs. However, it remains challenging in brain image registration due to the structural complexity of individual brains and the lack of ground truth for anatomical correspondences between the brain image pairs. This work devotes to achieving an end-to-end unsupervised brain deformable image registration method using the gyral-net map and 3D Res-Unet (BIRGU Net). Firstly, the gyral-net map was introduced to represent the 3D global cortex complex information of the brain image since it was considered as one of the anatomical landmarks, which can help to extract the salient structural feature of individual brains for registration. Secondly, the variant of 3D U-net architecture involving dual residual strategies was designed to map the image into the deformation field effectively and to prevent the gradient from vanishing as well. Finally, double regularized terms were imposed on the deformation field to guide the network training for leveraging the smoothness and the topology preservation of the deformation field. The registration procedure was trained in an unsupervised manner, which addressed the lack of ground truth for anatomical correspondences between the brain image pairs. The experimental results on four public data sets demonstrate that the extracted gyral-net can be an auxiliary feature for registration and the proposed network with the designed strategies can improve the registration performance since the Dice similarity coefficient (DSC) and normalized mutual information (NMI) are higher and the time consumption is comparable than the state-of-the-art. The code is available at https://github.com/mynameiswode/BIRGU-Net .

Boundary-Guided and Region-Aware Network With Global Scale-Adaptive for Accurate Segmentation of Breast Tumors in Ultrasound Images.

Breast ultrasound (BUS) image segmentation is a critical procedure in the diagnosis and quantitative analysis of breast cancer. Most existing methods for BUS image segmentation do not effectively utilize the prior information extracted from the images. In addition, breast tumors have very blurred boundaries, various sizes and irregular shapes, and the images have a lot of noise. Thus, tumor segmentation remains a challenge. In this article, we propose a BUS image segmentation method using a boundary-guided and region-aware network with global scale-adaptive (BGRA-GSA). Specifically, we first design a global scale-adaptive module (GSAM) to extract features of tumors of different sizes from multiple perspectives. GSAM encodes the features at the top of the network in both channel and spatial dimensions, which can effectively extract multi-scale context and provide global prior information. Moreover, we develop a boundary-guided module (BGM) for fully mining boundary information. BGM guides the decoder to learn the boundary context by explicitly enhancing the extracted boundary features. Simultaneously, we design a region-aware module (RAM) for realizing the cross-fusion of diverse layers of breast tumor diversity features, which can facilitate the network to improve the learning ability of contextual features of tumor regions. These modules enable our BGRA-GSA to capture and integrate rich global multi-scale context, multi-level fine-grained details, and semantic information to facilitate accurate breast tumor segmentation. Finally, the experimental results on three publicly available datasets show that our model achieves highly effective segmentation of breast tumors even with blurred boundaries, various sizes and shapes, and low contrast.

Identification for the cortical 3-Hinges folding pattern based on cortical morphological and structural features.

The Cortical 3-Hinges Folding Pattern (i.e., 3-Hinges) is one of the brain's hallmarks, and it is of great reference for predicting human intelligence, diagnosing eurological diseases and understanding the brain functional structure differences among gender. Given the significant morphological variability among individuals, it is challenging to identify 3-Hinges, but current 3-Hinges researches are mainly based on the computationally expensive Gyral-net method. To address this challenge, this paper aims to develop a deep network model to realize the fast identification of 3-Hinges based on cortical morphological and structural features. The main work includes: (1) The morphological and structural features of the cerebral cortex are extracted to relieve the imbalance between the number of 3-Hinges and each brain image's voxels; (2) The feature vector is constructed with the K nearest neighbor algorithm from the extracted scattered features of the morphological and structural features to alleviate over-fitting in training; (3) The squeeze excitation module combined with the deep U-shaped network structure is used to learn the correlation of the channels among the feature vectors; (4) The functional structure roles that 3-Hinges plays between adolescent males and females are discussed in this work. The experimental results on both adolescent and adult MRI datasets show that the proposed model achieves better performance in terms of time consumption. Moreover, this paper reveals that cortical sulcus information plays a critical role in the procedure of identification, and the cortical thickness, cortical surface area, and volume characteristics can supplement valuable information for 3-Hinges identification to some extent. Furthermore, there are significant structural differences on 3-Hinges among adolescent gender.

MCA-Net: Multi-Feature Coding and Attention Convolutional Neural Network for Predicting lncRNA-Disease Association.

With the advent of the era of big data, it is troublesome to accurately predict the associations between lncRNAs and diseases based on traditional biological experiments due to its time-consuming and subjective. In this paper, we propose a novel deep learning method for predicting lncRNA-disease associations using multi-feature coding and attention convolutional neural network (MCA-Net). We first calculate six similarity features to extract different types of lncRNA and disease feature information. Second, a multi-feature coding method is proposed to construct the feature vectors of lncRNA-disease association samples by integrating the six similarity features. Furthermore, an attention convolutional neural network is developed to identify lncRNA-disease associations under 10-fold cross-validation. Finally, we evaluate the performance of MCA-Net from different perspectives including the effects of the model parameters, distinct deep learning models, and the necessity of attention mechanism. We also compare MCA-Net with several state-of-the-art methods on three publicly available datasets, i.e., LncRNADisease, Lnc2Cancer, and LncRNADisease2.0. The results show that our MCA-Net outperforms the state-of-the-art methods on all three dataset. Besides, case studies on breast cancer and lung cancer further verify that MCA-Net is effective and accurate for the lncRNA-disease association prediction.

A Dynamic Parameter Noise-Tolerant Zeroing Neural Network for Time-Varying Quaternion Matrix Equation With Applications.

As a common and significant problem in the field of industrial information, the time-varying quaternion matrix equation (TV-QME) is considered in this article and addressed by an improved zeroing neural network (ZNN) method based on the real representation of the quaternion. In the light of an improved dynamic parameter (IDP) and an innovative activation function (IAF), a dynamic parameter noise-tolerant ZNN (DPNTZNN) model is put forward for solving the TV-QME. The presented IDP with the character of changing with the residual error and the proposed IAF with the remarkable performance can strongly enhance the convergence and robustness of the DPNTZNN model. Therefore, the DPNTZNN model possesses fast predefined-time convergence and superior robustness under different noise environments, which are theoretically analyzed in detail. Besides, the provided simulative experiments verify the advantages of the DPNTZNN model for solving the TV-QME, especially compared with other ZNN models. Finally, the DPNTZNN model is applied to image restoration, which further illustrates the practicality of the DPNTZNN model.

CRDet: Improving Signet Ring Cell Detection by Reinforcing the Classification Branch.

Detecting signet ring cells on histopathologic images is a critical computer-aided diagnostic task that is highly relevant to cancer grading and patients' survival rates. However, the cells are densely distributed and exhibit diverse and complex visual patterns in the image, together with the commonly observed incomplete annotation issue, posing a significant barrier to accurate detection. In this article, we propose to mitigate the detection difficulty from a model reinforcement point of view. Specifically, we devise a Classification Reinforcement Detection Network (CRDet). It is featured by adding a dedicated Classification Reinforcement Branch (CRB) on top of the architecture of Cascade RCNN. The proposed CRB consists of a context pooling module to perform a more robust feature representation by fully making use of context information, and a feature enhancement classifier to generate a superior feature by leveraging the deconvolution and attention mechanism. With the enhanced feature, the small-sized cell can be better characterized and CRDet enjoys a more accurate signet ring cell identification. We validate our proposal on a large-scale real clinical signet ring cell data set. It is shown that CRDet outperforms several popular convolutional neural network-based object detection models on this particular task.

Construction of arbitrary dimensional biorthogonal multiwavelet using lifting scheme.

This paper focuses on the construction of multidimensional biorthogonal multiwavelets and the perfect reconstruction multifilter banks. Based on the Hermite-Neville filter, two lifting structures have been proposed and systematically investigated, and a general design framework has been developed for building biorthogonal multiwavelets and Hermite interpolation filter banks with any multiplicity for any lattice in any dimension with any number of primal and dual vanishing moments. The construction is an important generalization of the Neville-based lifting scheme and inherits all of the advantages of lifting schemes such as fast transform, in-place computation and integer-to-integer transforms. Our multiwavelet systems preserve most of the desirable properties for applications, such as interpolating, short support, symmetry, and high vanishing moments.

Correction: Towards a Better Detection of Horizontally Transferred Genes by Combining Unusual Properties Effectively.

[This corrects the article DOI: 10.1371/journal.pone.0043126.].

Wavelet-domain TI Wiener-like filtering for complex MR data denoising.

Magnetic resonance (MR) images are affected by random noises, which degrade many image processing and analysis tasks. It has been shown that the noise in magnitude MR images follows a Rician distribution. Unlike additive Gaussian noise, the noise is signal-dependent, and consequently difficult to reduce, especially in low signal-to-noise ratio (SNR) images. Wirestam et al. in [20] proposed a Wiener-like filtering technique in wavelet-domain to reduce noise before construction of the magnitude MR image. Based on Wirestam's study, we propose a wavelet-domain translation-invariant (TI) Wiener-like filtering algorithm for noise reduction in complex MR data. The proposed denoising algorithm shows the following improvements compared with Wirestam's method: (1) we introduce TI property into the Wiener-like filtering in wavelet-domain to suppress artifacts caused by translations of the signal; (2) we integrate one Stein's Unbiased Risk Estimator (SURE) thresholding with two Wiener-like filters to make the hard-thresholding scale adaptive; and (3) the first Wiener-like filtering is used to filter the original noisy image in which the noise obeys Gaussian distribution and it provides more reasonable results. The proposed algorithm is applied to denoise the real and imaginary parts of complex MR images. To evaluate our proposed algorithm, we conduct extensive denoising experiments using T1-weighted simulated MR images, diffusion-weighted (DW) phantom and in vivo data. We compare our algorithm with other popular denoising methods. The results demonstrate that our algorithm outperforms others in term of both efficiency and robustness.

ProMT: effective human promoter prediction using Markov chain model based on DNA structural properties.

The core promoters play significant and extensive roles for the initiation and regulation of DNA transcription. The identification of core promoters is one of the most challenging problems yet. Due to the diverse nature of core promoters, the results obtained through existing computational approaches are not satisfactory. None of them considered the potential influence on performance of predictive approach resulted by the interference between neighboring TSSs in TSS clusters. In this paper, we sufficiently considered this main factor and proposed an approach to locate potential TSS clusters according to the correlation of regional profiles of DNA and TSS clusters. On this basis, we further presented a novel computational approach (ProMT) for promoter prediction using Markov chain model and predictive TSS clusters based on structural properties of DNA. Extensive experiments demonstrated that ProMT can significantly improve the predictive performance. Therefore, considering interference between neighboring TSSs is essential for a wider range of promoter prediction.

Lattice structure for generalized-support multidimensional linear phase perfect reconstruction filter bank.

Multidimensional linear phase perfect reconstruction filter bank (MDLPPRFB) can be designed and implemented via lattice structure. The lattice structure for the MDLPPRFB with filter support N(MΞ) has been published by Muramatsu , where M is the decimation matrix, Ξ is a positive integer diagonal matrix, and N(N) denotes the set of integer vectors in the fundamental parallelepiped of the matrix N. Obviously, if Ξ is chosen to be other positive diagonal matrices instead of only positive integer ones, the corresponding lattice structure would provide more choices of filter banks, offering better trade-off between filter support and filter performance. We call such resulted filter bank as generalized-support MDLPPRFB (GSMDLPPRFB). The lattice structure for GSMDLPPRFB, however, cannot be designed by simply generalizing the process that Muramatsu employed. Furthermore, the related theories to assist the design also become different from those used by Muramatsu . Such issues will be addressed in this paper. To guide the design of GSMDLPPRFB, the necessary and sufficient conditions are established for a generalized-support multidimensional filter bank to be linear-phase. To determine the cases we can find a GSMDLPPRFB, the necessary conditions about the existence of it are proposed to be related with filter support and symmetry polarity (i.e., the number of symmetric filters ns and antisymmetric filters na). Based on a process (different from the one Muramatsu used) that combines several polyphase matrices to construct the starting block, one of the core building blocks of lattice structure, the lattice structure for GSMDLPPRFB is developed and shown to be minimal. Additionally, the result in this paper includes Muramatsu's as a special case.

Towards a better detection of horizontally transferred genes by combining unusual properties effectively.

BACKGROUND: Horizontal gene transfer (HGT) is one of the major mechanisms contributing to microbial genome diversification. A number of computational methods for finding horizontally transferred genes have been proposed in the past decades; however none of them has provided a reliable detector yet. In existing parametric approaches, only one single compositional property can participate in the detection process, or the results obtained through each single property are just simply combined. It's known that different properties may mean different information, so the single property can't sufficiently contain the information encoded by gene sequences. In addition, the class imbalance problem in the datasets, which also results in great errors for the gene detection, hasn't been considered by the published methods. Here we developed an effective classifier system (Hgtident) that used support vector machine (SVM) by combining unusual properties effectively for HGT detection. RESULTS: Our approach Hgtident includes the introduction of more representative datasets, optimization of SVM model, feature selection, handling of imbalance problem in the datasets and extensive performance evaluation via systematic cross-validation methods. Through feature selection, we found that JS-DN and JS-CB have higher discriminating power for HGT detection, while GC1-GC3 and k-mer (k = 1, 2, …, 7) make the least contribution. Extensive experiments indicated the new classifier could reduce Mean error dramatically, and also improve Recall by a certain level. For the testing genomes, compared with the existing popular multiple-threshold approach, on average, our Recall and Mean error was respectively improved by 2.81% and reduced by 26.32%, which means that numerous false positives were identified correctly. CONCLUSIONS: Hgtident introduced here is an effective approach for better detecting HGT. Combining multiple features of HGT is also essential for a wider range of HGT events detection.

A novel hierarchical ensemble classifier for protein fold recognition.

The ensemble classifier plays a critical role in protein fold recognition. In this article, a novel hierarchical ensemble classifier named GAOEC (Genetic-Algorithm Optimized Ensemble Classifier) is presented and it can be constructed in the following steps. First, a novel optimized classifier named GAET-KNN (Genetic-Algorithm Evidence-Theoretic K Nearest Neighbors) is proposed as a component classifier. Second, six component classifiers in the first layer are used to get a potential class index for every query protein. Third, according to the results of the first layer, every component classifier in the second layer generates a 27-dimension vector whose elements represent the confidence degrees of 27-folds. Finally, genetic algorithm is used for generating weights for the outputs of the second layer to get the final classification result. The standard percentage accuracy of GAOEC is 64.7% on a widely used benchmark dataset, where the proteins in the testing set have less than 35% identity with those in the training set.