Multi-omic analysis of mandibuloacral dysplasia type A patient iPSC-derived MSC senescence reveals miR-311 as a novel biomarker for MSC senescence.

Mandibuloacral dysplasia type A (MADA) is a rare genetic progeroid syndrome associated with lamin A/C (LMNA) mutations. Pathogenic mutations of LMNA result in nuclear structural abnormalities, mesenchymal tissue damage and progeria phenotypes. However, it remains elusive how LMNA mutations cause mesenchymal-derived cell senescence and disease development. Here, we established an in vitro senescence model using induced pluripotent stem cell-derived mesenchymal stem cells (iMSCs) from MADA patients with homozygous LMNA p.R527C mutation. When expanded to passage 13 in vitro, R527C iMSCs exhibited marked senescence and attenuation of stemness potential, accompanied by immunophenotypic changes. Transcriptome and proteome analysis revealed that cell cycle, DNA replication, cell adhesion and inflammation might play important roles in senescence. In-depth evaluation of changes in extracellular vesicle (EV) derived iMSCs during senescence revealed that R527C iMSC-EVs could promote surrounding cell senescence by carrying pro-senescence microRNAs (miRNAs), including a novel miRNA called miR-311, which can serve as a new indicator for detecting chronic and acute mesenchymal stem cell (MSC) senescence and play a role in promoting senescence. Overall, this study advanced our understanding of the impact of LMNA mutations on MSC senescence and provided novel insights into MADA therapy as well as the link between chronic inflammation and aging development.

Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures.

BACKGROUND: Cell senescence is a sign of aging and plays a significant role in the pathogenesis of age-related disorders. For cell therapy, senescence may compromise the quality and efficacy of cells, posing potential safety risks. Mesenchymal stem cells (MSCs) are currently undergoing extensive research for cell therapy, thus necessitating the development of effective methods to evaluate senescence. Senescent MSCs exhibit distinctive morphology that can be used for detection. However, morphological assessment during MSC production is often subjective and uncertain. New tools are required for the reliable evaluation of senescent single cells on a large scale in live imaging of MSCs. RESULTS: We have developed a successful morphology-based Cascade region-based convolution neural network (Cascade R-CNN) system for detecting senescent MSCs, which can automatically locate single cells of different sizes and shapes in multicellular images and assess their senescence state. Additionally, we tested the applicability of the Cascade R-CNN system for MSC senescence and examined the correlation between morphological changes with other senescence indicators. CONCLUSIONS: This deep learning has been applied for the first time to detect senescent MSCs, showing promising performance in both chronic and acute MSC senescence. The system can be a labor-saving and cost-effective option for screening MSC culture conditions and anti-aging drugs, as well as providing a powerful tool for non-invasive and real-time morphological image analysis integrated into cell production.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

High-intensity focused ultrasound (HIFU) ablation versus surgical interventions for the treatment of symptomatic uterine fibroids: a meta-analysis.

OBJECTIVES: To compare the treatment success and safety of ultrasound- and MR-guided high-intensity focused ultrasound (HIFU) with surgery for treating symptomatic uterine fibroids. METHODS: We searched studies comparing HIFU with surgery for fibroids in different databases from January 2000 to July 2020. The mean difference (MD) or relative risk (RR) with 95% confidence interval (CI) for different outcome parameters was synthesized. RESULTS: We included 10 studies involving 4450 women. Compared with the surgery group, the decrease in uterine fibroid severity score at 6- and 12-month follow-up was higher in the HIFU group (MD - 4.16, 95% CI - 7.39 to - 0.94, and MD - 2.44, 95% CI - 3.67 to - 1.20, p < 0.05). The increase in quality-of-life (QoL) score at 6- and 12-month follow-up was higher in the HIFU group (MD 2.13, 95% CI 0.86 to 3.14, and MD 2.34, 95% CI 0.82 to 3.85, p < 0.05). The duration of hospital stay and the time to return to work was shorter in the HIFU group (MD - 3.41 days, 95% CI - 5.11 to - 1.70, and MD - 11.61 days, 95% CI - 19.73 to - 3.50, p < 0.05). The incidence of significant complications was lower in the HIFU group (RR 0.33, 95% CI 0.13 to 0.81, p < 0.05). The differences in the outcomes of adverse events, symptom recurrence, re-intervention, and pregnancy were not statistically significant (p > 0.05). CONCLUSIONS: HIFU is superior to surgery in terms of symptomatic relief, improvement in QoL, recovery, and significant complications. However, HIFU showed comparable effects to surgery regarding the incidence of adverse events, symptom recurrence, re-intervention, and pregnancy. KEY POINTS: • HIFU ablation is superior to surgery in terms of symptomatic relief, improvement in QoL, recovery, and significant complications. • HIFU has comparable effects to surgery in terms of symptom recurrence rate, re-intervention rate, and pregnancy rate, indicating that HIFU is a promising non-invasive therapy that seems not to raise the risk of recurrence and re-intervention or deteriorate fertility compared to surgical approaches in women with fibroids. • There is still a lack of good-quality comparative data and further randomized studies are necessary to provide sufficient and reliable data, especially on re-intervention rate and pregnancy outcome.

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.

Image-guided thermal ablation in the management of symptomatic adenomyosis: a systematic review and meta-analysis.

OBJECTIVE: To evaluate the clinical effects of image-guided thermal ablation for the treatment of symptomatic adenomyosis (AD). DATA SOURCES: We searched PubMed, Web of Science, Cochrane Library, EMBASE, ClinicalTrials.gov and Google Scholar for literature from January 2000 to September 2020. METHODS OF STUDY SELECTION: We included all studies reporting clinical outcomes of image-guided thermal ablation for AD, involving high-intensity focused ultrasound (HIFU), percutaneous microwave ablation (PMWA) and radiofrequency ablation (RFA). Two independent researchers performed study selection according to the screening criteria. RESULTS: A total of 38 studies representing 15,908 women were included. Compared with those at baseline, the visual analog scale scores, the symptom severity scores and the menorrhagia severity scores decreased significantly after these thermal ablation therapies. The mean ablation time was 92.18 min, 24.15 min and 31.93 min during HIFU, PMWA and RFA, respectively. The non-perfused volume ratio of AD was 68.3% for HIFU, 82.5% for PMWA and 79.2% for RFA. The reduction rates of uterine volume were 33.6% (HIFU), 46.8% (PMWA) and 44.0% (RFA). The reduction rates of AD volume were 45.1% (HIFU), 74.9% (PMWA) and 61.3% (RFA). The relief rates of dysmenorrhea were 84.2% (HIFU), 89.7% (PMWA) and 89.2% (RFA). The incidence of minor adverse events was 39.0% (HIFU), 51.3% (PMWA) and 3.6% (RFA). The re-intervention rates were 4.0% (HIFU) and 28.7% (RFA). The recurrence rate was 10.2% after HIFU. The pregnancy rates were 16.7% (HIFU), 4.93% (PMWA) and 35.8% (RFA). CONCLUSION: Image-guided HIFU, PMWA and RFA may be effective and safe minimally invasive therapies for symptomatic AD.

Ultrasound-guided Microwave Ablation in the Management of Symptomatic Uterine Myomas: A Systematic Review and Meta-analysis.

OBJECTIVE: This systematic review and meta-analysis aimed to evaluate the clinical effects and safety of ultrasound-guided microwave ablation (MWA) for the treatment of symptomatic uterine myomas. DATA SOURCES: We searched PubMed, Web of Science Core Collection, Cochrane Library, Embase, Scopus, and Google Scholar for studies from January 2000 to January 2021. METHODS OF STUDY SELECTION: We included all studies that reported the clinical outcomes of ultrasound-guided MWA in women with symptomatic uterine myomas. Two researchers conducted the study selection according to the screening criteria. TABULATION, INTEGRATION, AND RESULTS: We evaluated the risk of bias and evidence quality using the Newcastle-Ottawa scale. Two researchers independently extracted information from the included studies. We extracted the standardized mean difference (SMD) and pooled proportion with a 95% confidence interval (CI) for the outcome measures of interest. A total of 10 studies representing 671 patients were included. The Uterine Fibroid Symptom and Quality of Life (UFS-QoL) questionnaire was used to assess the clinical effects. Compared with baseline, the UFS scores decreased significantly (SMD 3.37; 95% CI, 2.27-4.47; p <.001; reduction rate 65.9%), QoL scores increased significantly (SMD -3.12; 95% CI, -3.93 to -2.30; p <.001; rate of increase 72.0%), and hemoglobin concentration increased significantly (SMD -2.13; 95% CI, -3.44 to -0.81; p = .002; rate of increase 30.3%) at follow-up. The mean operation time was 34.48 minutes (95% CI, 22.82-46.13; p <.001). The rate of reduction in myoma volume after MWA was 85.3% (95% CI, 82.7%-88.0%, p <.001). No major adverse event was reported, and the incidence of minor adverse events was 21.1% (95% CI, 15.1%-27.0%, p <.001). CONCLUSION: Ultrasound-guided MWA is an effective and safe minimally invasive therapy for symptomatic uterine myomas.

Uterine Artery Embolization Compared with High-intensity Focused Ultrasound Ablation for the Treatment of Symptomatic Uterine Myomas: A Systematic Review and Meta-analysis.

OBJECTIVE: This study aimed to compare the clinical effects of uterine artery embolization (UAE) with those of high-intensity focused ultrasound (HIFU) ablation for the treatment of symptomatic uterine myomas. DATA SOURCES: We searched PubMed, EMBASE, Web of Science, Cochrane Library, Google Scholar, and ClinicalTrials.gov for studies from January 2000 to August 2020. Related articles and relevant references of the included studies were also searched. METHODS OF STUDY SELECTION: Two researchers independently performed the data selection. We included comparative studies that compared the clinical outcomes of UAE with those of HIFU ablation in women with myomas. TABULATION, INTEGRATION, AND RESULTS: We assessed the study quality using the Cochrane Handbook for Systematic Reviews of Interventions for evaluating the risk of bias. Two independent researchers performed the article selection according to the screening criteria and rated the quality of evidence for each article. We calculated pooled mean difference with 95% confidence interval (CI) for continuous data and relative risk (RR) with 95% CI for dichotomous data. The systematic review registration number is CRD42020199630 on the International Prospective Register of Systematic Reviews. A total of 7 articles (5 trials), involving 4592 women with symptomatic uterine myomas, were included in the meta-analysis. Compared with the HIFU ablation group, the decrease in "uterine fibroid symptom" scores as well as the increase in quality-of-life scores at the time of follow-up were higher in the UAE group, with overall mean difference 19.54 (95% CI, 15.21-23.87; p <.001) and 15.72 (95% CI, 8.30-23.13; p <.001), respectively. The women in the UAE group had a significantly lower reintervention rate (RR 0.25; 95% CI, 0.15-0.42; p <.001). The women undergoing UAE had a significantly lower pregnancy rate than those undergoing HIFU ablation (RR 0.06; 95% CI, 0.01-0.45; p = .006). The difference in the incidence of adverse events between the 2 groups was not statistically significant (p = .53). CONCLUSION: Compared with HIFU ablation, UAE provided more significant alleviation of symptoms and improvement in quality of life, lower postoperative reintervention rate, and lower pregnancy rate for women with uterine myomas. However, we cannot conclude that HIFU ablation is more favorable for desired pregnancy than UAE because of the confounding factors.

Augmented Multicenter Graph Convolutional Network for COVID-19 Diagnosis.

Chest computed tomography (CT) scans of coronavirus 2019 (COVID-19) disease usually come from multiple datasets gathered from different medical centers, and these images are sampled using different acquisition protocols. While integrating multicenter datasets increases sample size, it suffers from inter-center heterogeneity. To address this issue, we propose an augmented multicenter graph convolutional network (AM-GCN) to diagnose COVID-19 with steps as follows. First, we use a 3-D convolutional neural network to extract features from the initial CT scans, where a ghost module and a multitask framework are integrated to improve the network's performance. Second, we exploit the extracted features to construct a multicenter graph, which considers the intercenter heterogeneity and the disease status of training samples. Third, we propose an augmentation mechanism to augment training samples which forms an augmented multicenter graph. Finally, the diagnosis results are obtained by inputting the augmented multi-center graph into GCN. Based on 2223 COVID-19 subjects and 2221 normal controls from seven medical centers, our method has achieved a mean accuracy of 97.76%. The code for our model is made publicly.1.

Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.

BACKGROUND: Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems. OBJECTIVE: This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks. METHODS: In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets. RESULTS: The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems. CONCLUSIONS: From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.

Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network.

Prediction of protein-protein interactions (PPIs) is of great significance. To achieve this, we propose a novel computational method for PPIs prediction based on a similarity network fusion (SNF) model for integrating the physical and chemical properties of proteins. Specifically, the physical and chemical properties of protein are the protein amino acid mutation rate and its hydrophobicity, respectively. The amino acid mutation rate is extracted using a BLOSUM62 matrix, which puts the protein sequence into block substitution matrix. The SNF model is exploited to fuse protein physical and chemical features of multiple data by iteratively updating each original network. Finally, the complementary features from the fused network are fed into a label propagation algorithm (LPA) for PPIs prediction. The experimental results show that the proposed method achieves promising performance and outperforms the traditional methods for the public dataset of H. pylori, Human, and Yeast. In addition, our proposed method achieves average accuracy of 76.65%, 81.98%, 84.56%, 84.01% and 84.38% on E. coli, C. elegans, H. sapien, H. pylori and M. musculus datasets, respectively. Comparison results demonstrate that the proposed method is very promising and provides a cost-effective alternative for predicting PPIs. The source code and all datasets are available at http://pan.baidu.com/s/1dF7rp7N.

Prior-Guided Adversarial Learning With Hypergraph for Predicting Abnormal Connections in Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnostic methods have accomplished the classification task, but few of them can accurately evaluate the changing characteristics of brain connectivity. In this work, a prior-guided adversarial learning with hypergraph (PALH) model is proposed to predict abnormal brain connections using triple-modality medical images. Concretely, a prior distribution from anatomical knowledge is estimated to guide multimodal representation learning using an adversarial strategy. Also, the pairwise collaborative discriminator structure is further utilized to narrow the difference in representation distribution. Moreover, the hypergraph perceptual network is developed to effectively fuse the learned representations while establishing high-order relations within and between multimodal images. Experimental results demonstrate that the proposed model outperforms other related methods in analyzing and predicting AD progression. More importantly, the identified abnormal connections are partly consistent with previous neuroscience discoveries. The proposed model can evaluate the characteristics of abnormal brain connections at different stages of AD, which is helpful for cognitive disease study and early treatment.

A New Brain Network Construction Paradigm for Brain Disorder Via Diffusion-Based Graph Contrastive Learning.

Brain network analysis plays an increasingly important role in studying brain function and the exploring of disease mechanisms. However, existing brain network construction tools have some limitations, including dependency on empirical users, weak consistency in repeated experiments and time-consuming processes. In this work, a diffusion-based brain network pipeline, DGCL is designed for end-to-end construction of brain networks. Initially, the brain region-aware module (BRAM) precisely determines the spatial locations of brain regions by the diffusion process, avoiding subjective parameter selection. Subsequently, DGCL employs graph contrastive learning to optimize brain connections by eliminating individual differences in redundant connections unrelated to diseases, thereby enhancing the consistency of brain networks within the same group. Finally, the node-graph contrastive loss and classification loss jointly constrain the learning process of the model to obtain the reconstructed brain network, which is then used to analyze important brain connections. Validation on two datasets, ADNI and ABIDE, demonstrates that DGCL surpasses traditional methods and other deep learning models in predicting disease development stages. Significantly, the proposed model improves the efficiency and generalization of brain network construction. In summary, the proposed DGCL can be served as a universal brain network construction scheme, which can effectively identify important brain connections through generative paradigms and has the potential to provide disease interpretability support for neuroscience research.

Cell classification with worse-case boosting for intelligent cervical cancer screening.

Cell classification underpins intelligent cervical cancer screening, a cytology examination that effectively decreases both the morbidity and mortality of cervical cancer. This task, however, is rather challenging, mainly due to the difficulty of collecting a training dataset representative sufficiently of the unseen test data, as there are wide variations of cells' appearance and shape at different cancerous statuses. This difficulty makes the classifier, though trained properly, often classify wrongly for cells that are underrepresented by the training dataset, eventually leading to a wrong screening result. To address it, we propose a new learning algorithm, called worse-case boosting, for classifiers effectively learning from under-representative datasets in cervical cell classification. The key idea is to learn more from worse-case data for which the classifier has a larger gradient norm compared to other training data, so these data are more likely to correspond to underrepresented data, by dynamically assigning them more training iterations and larger loss weights for boosting the generalizability of the classifier on underrepresented data. We achieve this idea by sampling worse-case data per the gradient norm information and then enhancing their loss values to update the classifier. We demonstrate the effectiveness of this new learning algorithm on two publicly available cervical cell classification datasets (the two largest ones to the best of our knowledge), and positive results (4% accuracy improvement) yield in the extensive experiments. The source codes are available at: https://github.com/YouyiSong/Worse-Case-Boosting.

BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment.

Effective connectivity estimation plays a crucial role in understanding the interactions and information flow between different brain regions. However, the functional time series used for estimating effective connectivity is derived from certain software, which may lead to large computing errors because of different parameter settings and degrade the ability to model complex causal relationships between brain regions. In this paper, a brain diffuser with hierarchical transformer (BDHT) is proposed to estimate effective connectivity for mild cognitive impairment (MCI) analysis. To our best knowledge, the proposed brain diffuser is the first generative model to apply diffusion models to the application of generating and analyzing multimodal brain networks. Specifically, the BDHT leverages structural connectivity to guide the reverse processes in an efficient way. It makes the denoising process more reliable and guarantees effective connectivity estimation accuracy. To improve denoising quality, the hierarchical denoising transformer is designed to learn multi-scale features in topological space. By stacking the multi-head attention and graph convolutional network, the graph convolutional transformer (GraphConformer) module is devised to enhance structure-function complementarity and improve the ability in noise estimation. Experimental evaluations of the denoising diffusion model demonstrate its effectiveness in estimating effective connectivity. The proposed model achieves superior performance in terms of accuracy and robustness compared to existing approaches. Moreover, the proposed model can identify altered directional connections and provide a comprehensive understanding of parthenogenesis for MCI treatment.

Federated learning using model projection for multi-center disease diagnosis with non-IID data.

Multi-center disease diagnosis aims to build a global model for all involved medical centers. Due to privacy concerns, it is infeasible to collect data from multiple centers for training (i.e., centralized learning). Federated Learning (FL) is a decentralized framework that enables multiple clients (e.g., medical centers) to collaboratively train a global model while retaining patient data locally for privacy. However, in practice, the data across medical centers are not independently and identically distributed (Non-IID), causing two challenging issues: (1) catastrophic forgetting at clients, i.e., the local model at clients will forget the knowledge received from the global model after local training, causing reduced performance; and (2) invalid aggregation at the server, i.e., the global model at the server may not be favorable to some clients after model aggregation, resulting in a slow convergence rate. To mitigate these issues, an innovative Federated learning using Model Projection (FedMoP) is proposed, which guarantees: (1) the loss of local model on global data does not increase after local training without accessing the global data so that the performance will not be degenerated; and (2) the loss of global model on local data does not increase after aggregation without accessing local data so that convergence rate can be improved. Extensive experimental results show that our FedMoP outperforms state-of-the-art FL methods in terms of accuracy, convergence rate and communication cost. In particular, our FedMoP also achieves comparable or even higher accuracy than centralized learning. Thus, our FedMoP can ensure privacy protection while outperforming centralized learning in accuracy and communication cost.

Estimating Addiction-Related Brain Connectivity by Prior-Embedding Graph Generative Adversarial Networks.

The study of nicotine addiction mechanism is of great significance in both nicotine withdrawal and brain science. The detection of addiction-related brain connectivity using functional magnetic resonance imaging (fMRI) is a critical step in study of this mechanism. However, it is challenging to accurately estimate addiction-related brain connectivity due to the low-signal-to-noise ratio of fMRI and the issue of small sample size. In this work, a prior-embedding graph generative adversarial network (PG-GAN) is proposed to capture addiction-related brain connectivity accurately. By designing a dual-generator-based scheme, the addiction-related connectivity generator is employed to learn the feature map of addiction connection, while the reconstruction generator is used for sample reconstruction. Moreover, a bidirectional mapping mechanism is designed to maintain the consistency of sample distribution in the latent space so that addiction-related brain connectivity can be estimated more accurately. The proposed model utilizes prior knowledge embeddings to reduce the search space so that the model can better understand the latent distribution for the issue of small sample size. Experimental results demonstrate the effectiveness of the proposed PG-GAN.

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).

3D Multimodal Fusion Network with Disease-induced Joint Learning for Early Alzheimer's Disease Diagnosis.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images.

BACKGROUND: Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity. METHODS: In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios. RESULTS: Our baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks. CONCLUSIONS: Comprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.