MicroRNAs (miRNAs) are small non-coding RNAs orchestrating regulatory networks through sequence-specific target recognition. Understanding miRNA-disease correlations is crucial as high-throughput sequencing data growth outpaces experimental validation, necessitating computational approaches for association discovery. Existing frameworks model miRNA-disease interactions as uniform binary relationships, overlooking semantic diversity in different association mechanisms. We propose BKAMDA (MiRNA-Disease Associations prediction Based on Knowledge-Awareness), a novel knowledge-aware model for predicting miRNA-disease associations. Unlike existing methods learning only from miRNA-disease networks, BKAMDA leverages knowledge graphs to delineate distinct association types. By simulating informational propagation within knowledge graphs across diverse miRNA-disease relationships, the model investigates latent connections across various relationship types. Comparative analysis with competitive baselines using real-world experimentally validated datasets demonstrates excellent performance across multiple metrics. Three disease case studies further confirm model accuracy and effectiveness for precision medicine applications. Our knowledge-aware approach significantly advances miRNA-disease association prediction by capturing semantic diversity in biological interactions.

Remote photoplethysmography (rPPG) provides a convenient solution for contactless physiological measurement, and is a promising technique for daily health monitoring and clinical application. However, traditional supervised methods rely heavily on labeled data, incurring substantial annotation costs. Current unsupervised contrastive learning methods encounter challenges under challenging conditions, including variations in illumination and motion of the head or limbs. In this paper, we propose a novel unsupervised end-to-end framework, called CoDPN for physiological measurement. CoDPN employs a dual-path architecture consisting of the Short-term Information Extraction Path (SIEP) and Long-term Information Extraction Path (LIEP), which capture short-term contextual relevance and long-term periodic dependence of rPPG signals, respectively. Next, we propose a collaborative learning strategy to integrate the latent features from both the SIEP and LIEP, facilitating the exchange of complementary information. Furthermore, our unsupervised learning strategy leverages rPPG features in both the frequency and time domains, guiding the CoDPN to extract rPPG signals consistent with physiological information while reducing dependence on labeled datasets. We conduct extensive experiments on three benchmark datasets (UBFC-rPPG, PURE, and UBFC-phys) to implement physiological measurement, including heart rate (HR), heart rate variability (HRV) and respiratory frequency (RF). The experimental results demonstrate that our CoDPN outperforms other state-of-the-art methods under complex conditions.

Network Function Virtualization (NFV) facilitates on-demand and flexible service provisioning to meet the escalating demands of Internet of Things (IoT) applications, enabled by Service Function Chain (SFC) technique. The widespread deployment of 5G has connected a massive number of devices and users to IoT networks, accelerating the expansion of IoT scales. IoT users’ service requirements exhibit heightened diversity and dynamism. Consequently, the SFC placement problem in Next Generation Multi-domain IoT (NGMIoT) networks has garnered significant attention. How to efficiently place SFCs under uncertain resource demands to adapt to evolving service request dynamics poses substantial challenges. Therefore, this paper investigates the Robust SFC Placement (RSFCP) problem in NGMIoT networks under resource demand uncertainty. Specifically, we formulate the RSFCP problem as an integer linear programming model to minimize overall SFC placement cost while ensuring service quality. We further prove the RSFCP problem is NP-hard and propose a greedy strategy based heuristic SFC placement algorithm to solve it. Finally, extensive simulation experiments are conducted to evaluate performance, demonstrating that the proposed algorithm outperforms benchmark mechanisms in terms of service acceptance rate and placement cost.

Background and Objective. Currently, depression is a widespread global issue that imposes a significant burden and disability on individuals, families, and society. Deep learning (DL) has emerged as a valuable approach for automatically detecting depression by extracting cues from audiovisual data and making a diagnosis. PHQ-8 is considered a validated diagnostic tool for depressive disorders in clinical studies, and the objective of this experiment is to improve the accuracy of PHQ-8 prediction. Furthermore, this paper aims to demonstrate the effectiveness of expert knowledge in depression diagnosis and discuss a novel multimodal network architecture. Methods. This research paper focuses on multimodal depression analysis, proposing a flexible parallel transformer (FPT) model capable of extracting data from three distinct modalities (i.e., one video and two audio descriptors). The FPT-Former model incorporates three paths, each using expert-knowledge-based descriptors from one modality as inputs. These descriptors are represented into 32 features by the encoder part of a transformer module, and these features are fused to realize the final regression of PHQ-8 score. The extended distress analysis interview corpus (E-DAIC) is an expansion of WOZ-DAIC which comprises semiclinical interviews intended to assist in the diagnosis of psychological distress conditions. It encompasses a sample size of 275 participants, and in this study, it was utilized to test the model in a way of 10-fold cross-validation. Results. The FPT presented herein achieved comparable performance to the state-of-the-art works, with a root mean square error (RMSE) of 4.80 and a mean absolute error (MAE) of 4.58. The ablation experiments demonstrate that the three-modality-fused model outperforms other two-modality-fused and single-modality models. While using a PHQ-8 score threshold of 10, the accuracy of the depression classification is 0.79. Conclusions. Leveraging the strength of expert-knowledge-based multimodal measures and parallel transformer structure, the FPT model exhibits promising performance in depression detection. This model improved the accuracy of depression diagnosis through audio and video, and it also proved the effectiveness of using expert-knowledge in the diagnosis of depression. The traits of flexible structure, high predictive efficiency, and secure privacy protection make our model a promotable intelligent system in mental healthcare.

Unsupervised domain adaptation (UDA) methods have shown great potential in cross-modality medical image segmentation tasks, where target domain labels are unavailable. However, the domain shift among different image modalities remains challenging, because the conventional UDA methods are based on convolutional neural networks (CNNs), which tend to focus on the texture of images and cannot establish the global semantic relevance of features due to the locality of CNNs. This paper proposes a novel end-to-end Swin Transformer-based generative adversarial network (ST-GAN) for cross-modality cardiac segmentation. In the generator of ST-GAN, we utilize the local receptive fields of CNNs to capture spatial information and introduce the Swin Transformer to extract global semantic information, which enables the generator to better extract the domain-invariant features in UDA tasks. In addition, we design a multi-scale feature fuser to sufficiently fuse the features acquired at different stages and improve the robustness of the UDA network. We extensively evaluated our method with two cross-modality cardiac segmentation tasks on the MS-CMR 2019 dataset and the M&Ms dataset. The results of two different tasks show the validity of ST-GAN compared with the state-of-the-art cross-modality cardiac image segmentation methods.

Early screening of PDAC (pancreatic ductal adenocarcinoma) based on plain CT (computed tomography) images is of great significance. Therefore, this work conducted a radiomics-aided diagnosis analysis of PDAC based on plain CT images. We explored a novel MSTA (multiresolution-statistical texture analysis) architecture to extract texture features and built machine learning models to classify PDACs and HPs (healthy pancreases). We also performed significance tests of differences to analyze the relationships between histopathological characteristics and texture features. The MSTA architecture originates from the analysis of histopathological characteristics and combines multiresolution analysis and statistical analysis to extract texture features. The MSTA architecture achieved better experimental results than the traditional architecture that scales the coefficient matrices of the multiresolution analysis. In the validation of the classifications, the MSTA architecture achieved an accuracy of 81.19% and an AUC (area under the ROC (receiver operating characteristic) curve) of 0.88 (95% confidence interval: 0.84-0.92). In the test of the classifications, it achieved an accuracy of 77.66% and an AUC of 0.79 (95% confidence interval: 0.71-0.87). Moreover, the significance tests of differences showed that the extracted texture features may be relevant to the histopathological characteristics. The MSTA architecture is beneficial for the radiomics-aided diagnosis of PDAC based on plain CT images. Its texture features can potentially enhance radiologists' imaging interpretation abilities.

High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer-aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography image transformation, the lung nodule segmentation, and the feature extraction, to construct a whole CADe system. It is difficult for these schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multigroup patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multigroup patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.

Heart disease is one of the most severe health illnesses. Developing accurate and efficient methods to diagnose heart disease is crucial in providing good heart healthcare to patients. In this paper, a data mining based technique for diagnosing heart disease is introduced, in which heart disease related patient data sets are utilized. A matrix factorization based technique for missing data reconstruction is presented. Numerical results show that recovery data sets are able to achieve reliable diagnosis or classification performance comparable to using original completed patient datasets.

Directional features are extremely important in diagnostic imaging. They can be used either in the searching for important indications of abnormality or for the reference in image alignment. The wavelet transform (WT) as an efficient method for multiresolution representation has been used extensively in medical imaging. However, from the view point of directional selectivity it is not so efficient in feature extraction. In this paper, an analysis and comparison of directional selectivity of wavelet transforms with three different frequency decompositions were undertaken. WTs with radial-angular decomposition were used for two medical imaging problems: mass detection in digital mammography and lung nodule detection in chest radiography. The results demonstrated its efficiency.

Multi-resolution and multi-orientation are part of the biological mechanism of human visual system. The interest in multi-resolution image analysis has been growing rapidly. Comparatively, however, the multi-orientation method has received less attention. Based on the relationship between multifrequency channel decomposition and the wavelet model developed by Mallat [1989], a combined multiresolution/multi-orientation representation by directional wavelet transform (DWT) is introduced in this paper. Because of its high orientation selectivity, the DWT allows the processing of directional information more efficiently. It is applied successfully to feature enhancement and extraction of X-ray mammographic images.

A novel wavelet-based neural network with fuzzy-logic adaptivity (WNNFA) is proposed for image restoration using a nuclear medicine gamma camera based on the measured system point spread function. The objective is to restore image degradation due to photon scattering and collimator photon penetration with the gamma camera and allow improved quantitative external measurements of radionuclides in vivo. The specific clinical model proposed is the imaging of bremsstrahlung radiation using /sup 32/P and /sup 90/Y. The theoretical basis for four-channel multiresolution wavelet decomposition of the nuclear image into different subimages is developed with the objective of isolating the signal from noise. A fuzzy rule is generated to train a membership function using least mean squares to obtain an optimal balance between image restoration and the stability of the neural network, while maintaining a linear response for the camera to radioactivity dose. A multichannel modified Hopfield neural network architecture is then proposed for multichannel image restoration using the dominant signal subimages.

A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCCs) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCCs and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP's MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data is analyzed.

A novel hybrid filter architecture is proposed that incorporates an adaptive multistage nonlinear filter (AMNF) and a tree-structured wavelet transform (TSWT) specifically designed for image enhancement in medical imaging using different sensors. The AMNF is used for noise suppression and image enhancement. The TSWT is used for multiresolution decomposition and reconstruction of the medical images where selective reconstruction of subimages is used to perform further enhancement of specific clinical features. The AMNF-TSWT filter produced enhanced images for different modalities namely, digital mammography, chest, skin cancer and retinal images, that provided improved visualization of specific clinical features compared to the raw digital images. The AMNF-TSWT filter should prove to be useful for the use of neural networks for feature classification using different sensors and imaging protocols.>

A new class of nonlinear filters with more robust characteristics for noise suppression and detail preservation is proposed for processing digital mammographic images. The new algorithm consists of two major filtering blocks: (a) a multistage tree-structured filter for image enhancement that uses central weighted median filters as basic sub-filtering blocks and (b) a dispersion edge detector. The design of the algorithm also included the use of linear and curved windows to determine whether variable shape windowing could improve detail preservation. First, the noise-suppressing properties of the tree-structured filter were compared to single filters, namely the median and the central weighted median with conventional square and variable shape adaptive windows; simulated images were used for this purpose. Second, the edge detection properties of the tree-structured filter cascaded with the dispersion edge detector were compared to the performance of the dispersion edge detector alone, the Sobel operator, and the single median filter cascaded with the dispersion edge detector. Selected mammographic images with representative biopsy-proven malignancies were processed with all methods and the results were visually evaluated by an expert mammographer. In all applications, the proposed filter suggested better detail preservation, noise suppression, and edge detection than all other approaches and it may prove to be a useful tool for computer-assisted diagnosis in digital mammography.

An order statistic and neural network hybrid filter (OSNNH) is proposed for the restoration of gamma camera images using the measured modulation transfer function. Planar images of beta-emitting radionuclides are used to evaluate the filter because they exhibit higher degradation than images of single photon emitters due to increased photon scattering and collimator septal penetration. The filter performance is quantitatively evaluated and compared to that of the Wiener filter by investigating the relationship between the externally measured counts from sources of phosphorous-32 ((32)P) at various depths in water. An effective linear attenuation coefficient for (32)P is determined to be equal to 0.13 cm(-1) and 0.14 cm(-1) for the OSNNH and the Wiener filters, respectively. Evaluation of phantom and patient filtered images demonstrates that the OSNNH filter avoids ring effects caused by the ill-conditioned blur matrix and noise overriding caused by matrix inversion, typical of other restoration filters such as the Wiener.