Parkinson's disease (PD) and Alzheimer's disease (AD) are the two most prevalent and incurable neurodegenerative diseases (NDs) worldwide, for which early diagnosis is critical to delay their progression. However, the high dimensionality of multi-metric data with diverse structural forms, the heterogeneity of neuroimaging and phenotypic data, and class imbalance collectively pose significant challenges to early ND diagnosis. To address these challenges, we propose a dynamically weighted dual graph attention network (DW-DGAT) that integrates: (1) a general-purpose data fusion strategy to merge three structural forms of multi-metric data; (2) a dual graph attention architecture based on brain regions and inter-sample relationships to extract both micro- and macro-level features; and (3) a class weight generation mechanism combined with two stable and effective loss functions to mitigate class imbalance. Rigorous experiments, based on the Parkinson Progression Marker Initiative (PPMI) and Alzhermer's Disease Neuroimaging Initiative (ADNI) studies, demonstrate the state-of-the-art performance of our approach.

Diffusion probabilistic models (DPMs) have recently demonstrated promising performance in medical image segmentation. However, traditional DPM has difficulty handling the irregular structure of images and the inherent similarity between lesions and surrounding tissues. To overcome these challenges, we propose an innovative architecture, the multiscale Mamba DPM (MsM-DPM), designed to enhance medical image segmentation. Specifically, MsM-DPM introduces a multiscale attention fusion module (MSAFM) in a multiscale denoising UNet (Ms-DU) to capture lesion deformations from multilevel features, thereby enhancing the model's robustness to shape and scale variations. Furthermore, in the segmentation network, a multilayer axial feature module (MLAFM) is used to adaptively aggregate the global context features from the Mamba encoder to enhance the expression of features in the spatial dimension by capturing axial multiscale features. The multilevel global context (MLGC) module is then used to reconstruct skip connections using graph convolutional network inference, and the enhanced features are assigned to each layer in the decoder to capture the contextual relationship of features. Finally, the feature fusion module (FFM) integrates deep features with upsampled features in the decoder, enhancing the network's ability to capture lesion boundary details. Our MsM-DPM effectively encodes the semantic difference between lesions and background to improve the representation of their internal features. Extensive experiments on six datasets, LUNA16, ATM22, COVID-19, Self-collected datasets, Pancreas, and BT-MSD, show that the proposed MsM-DPM outperforms existing segmentation methods. Our code is publicly available at https://github.com/suhuaqiang/deep-learning.

Parkinson's disease (PD) is a progressive and currently incurable neurological disorder, where early diagnosis plays a critical role in slowing disease progression. Multi-metric data from multimodal neuroimages provide complementary perspectives that can enhance early PD diagnosis. In this paper, we propose a Self-Supervised Learning Dual Attention Network (SSL-DAN) to address the uncertainty of key metrics and brain regions of interest (ROIs), the global dependencies among ROIs within each metric, and the information conflicts arising from the multi-branch architecture. Extensive experiments conducted on the Parkinson's Progression Markers Initiative study demonstrate the effectiveness of the proposed method.

Parkinson's disease (PD) is a neurodegenerative disease that frequently occurs in middle-aged and elderly people. Early diagnosis is significant in slowing its progression, and attention mechanisms and graph convolutional networks (GCNs) in the deep learning method can extract effective features for early diagnosis. However, existing attention mechanisms with fixed receptive fields or scales fail to focus on local details and crucial global patterns in parallel, and a conventional single-view GCN is insufficient for revealing graph features in both brain regions and sample relationships. Based on the above, we propose coupled multi-view GCNs based on multi-scale attention and knowledge embedding. First, the brain connectivity networks generated from diffusion tensor imaging (DTI) are input into the proposed multi-scale hybrid attention (MSHA) module to emphasize distinct channel and spatial information at multiple scales. Subsequently, we devise multi-view brain region GCNs (MVBRGs) to extract representations of brain regions from different perspectives. Finally, we design multi-view sample GCNs (MVSGs) that incorporate phenotypic data as knowledge embedding to discover critical features of samples for final dichotomous classifications. The experimental results based on the Parkinson's Progression Markers Initiative (PPMI) data source demonstrate that our approach achieves an average accuracy of 88.74% in PD diagnosis, exhibiting state-of-the-art performance.

Fundus image is vital for the diagnosis and monitoring of various eye diseases, where the accuracy of diagnostic results is largely determined by the quality of the obtained images. However, the performance of existing Fundus Image Quality Assessment(FIQA) methods is primarily constrained by the quantity and quality of retinal datasets, highlighting a significant challenge in the field due to the absence of a large annotated quality dataset. To alleviate this problem, we purpose the Global-Local Denoising Auto-Encoder(GL-DAE), a novel and efficient self-supervised learning pre-training approach in FIQA to overcome the scarcity of annotated fundus images. Specifically, in the pre-training phase, given a batch of unlabeled fundus images, we apply different distortion algorithms to degrade the original fundus images. The degraded fundus images are then subjected to global and local cropping, and subsequently, global and local features are extracted using a joint encoder. By reconstructing global and local fundus images, the model learns to extract features related to fundus quality. Afterwards, to complement the global reconstruction task, we further design a noise identification based contrative learning to guide the models to identify different types of degradation algorithm. Fine-tuning results across two FIQA datasets highlight the superiority of our method compared to other classical image classification methods, demonstrating its potential to advance the reliability of FIQA.

Parkinson’s disease (PD) is a common and irreversible neurodegenerative disease that the earlier it is diagnosed, the easier and better it can be controlled. This paper proposes a self-supervised distillation and multi-scale dynamic convolutional network for PD classification. Experiments indicate that our proposed method has outperformed most SOTA methods.

Fundus images are commonly used to document the presence and severity of various retinal degenerative diseases, where the fovea, optic disc (OD), and optic cup (OC) serve as important anatomical landmarks. Locating and segmenting these landmarks are crucial for clinical diagnosis and treatment. Many existing methods treat the recognition of the fovea, OD, and OC as separate tasks without incorporating any clinical prior knowledge related to various anatomical structures. In this paper, we propose a prior information guided coarse-to-fine dual-branch encoding network, which enables fovea localization and OD/OC segmentation. In coarse stage, we employ a dual-branch network consisting of convolutional neural network (CNN) and Transformer to encode local and global features, and then utilize multi-scale feature fusion techniques to merge the extracted semantic features, aiming to enhance the localization accuracy. In addition, we effectively use the distance information from each pixel to the landmark of interest, and output the results of distance map and heat map regression as prior information to further guide the network to learn the positional relationship between fovea and OD. In fine stage, we refine the region of interest (ROI) of the OD, balance the distribution of the OD and OC using polar coordinate transformation (PCT), extract critical boundary features using the boundary attention module (BAM), and improve the generalization performance of our method through model ensemble strategy. Extensive experimental results demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) methods on the publicly available GAMMA and REFUGE datasets.

Multiple Myeloma (MM) is a growing global health concern, and early diagnosis is crucial for effective treatment. Efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Microscopic images have high resolution, where cells are enormous and dense. Therefore, the annotation process is time-consuming and complex for tasks such as segmentation due to pixel-level marking. In this paper, we design an end-to-end weakly supervised myeloma cell segmentation framework based on point annotation. It can achieve accurate cell segmentation comparable to fully supervised methods while reducing the need for manual annotation, greatly shortening annotation time. Experimental results demonstrate that our method achieves 98% of its fully-supervised performance with only 10 annotated random points per instance, and outperforms the fully-supervised Mask RCNN.

Accurate classification of coronary artery plaques can provide effective assistance for the diagnosis of coronary artery disease(CAD). The task of coronary artery plaque classification remains extremely challenging due to the complex anatomical structure and background of coronary arteries. 3D convolution still has limitations in feature modeling, so this study builds a dual branch bridge network based on convolution neural network (CNN) and Transformer framework, and fused the local feature extraction ability of convolution and the global modeling ability of Transformer through the bridge communication module. By using a shift attention (SA) module at the intersection of dual branch information to utilizes minimal computational complexity to fuse feature maps from both branches. The ghost plus (GP) module was aimed at balancing the enormous computational power issues of building rich semantic information and training difficulties in 3D Transformers. The proposed method have demonstrated the effectiveness through a large number of comparative and ablation experiment.

Diagnosing retinopathy of prematurity (ROP) is a time-consuming and complex task, even for experienced clinicians, as it is challenging to determine its specific stages accurately. In this study, we propose an advanced dual-branch feature interaction network for predicting the stages of ROP using color fundus photographs. Specifically, the proposed network includes a Vision Transformer (ViT) branch and a convolutional neural network (CNN) branch, which are used to capture global contextual information and express local detail features, respectively. To improve the efficiency of ViT, we introduce a cascaded group attention (CGA) module feeding attention heads with different splits of the full feature, which not only saves computation cost but also improves attention diversity. The semantic features extracted from both the Transformer and CNN branches are fused through the branch feature interaction (BFI) module, allowing us to leverage the unique characteristics of both branches to optimize ROP feature representations comprehensively. Afterwards, we further design a Transformer block with structure information learning (SIL) to gather ROP-related semantic information from high-level features, gradually constructing ROP feature information structure to highlight important regions and improve the model’s discriminative ability for different lesion feature structures. Our extensive experiments on both clinical and public datasets produce promising results, showcasing the outstanding performance of our method.

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network’s ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16.

Macular fovea localization in fundus images is a critical stage for computer-aided diagnostic techniques of many retinal diseases. Due to its cluttered visual characteristics, it is difficult to accurately locate the fovea. Many previous methods obtain the location of macular fovea from pre-extracting image features extracted from surrounding structures, such as optic disc and vascular distribution. Deep learning-based regression techniques are promising due to their effective modeling of the relationship between the fovea and its surrounding structure for fovea localization. However, there are still many challenges to locate the fovea using deep learning accurately. To address these issues, we design a novel end-to-end multi-task learning regression network for fovea localization. Specifically, the proposed network consists of two regression networks. For the coordinate regression network, we introduce multi-scale fusion technology and a multi-head self-attention module to extract discriminative context information and capture long-term dependence, respectively. For the heatmap regression network, the generated heatmap according to the coordinates is utilized to supervise the output of the network. The experimental results on three public datasets demonstrate that our method achieves superior performance for the localization of macular fovea.

Parkinson’s disease (PD) is a neurodegenerative geriatric disease commonly occurring in middle-aged and elderly adults. Since PD is irreversible and its treatment only slows down its rate of development, the early diagnosis by accurate prediction is of great significance to retard its deterioration. However, the existing computer-assisted diagnosis methods for PD have limitations in exploring the implicit and spatial information in the brain. In view of this limitation, a 3D network based on self-supervised learning strategy and attention mechanism is proposed for PD classification in this paper. The proposed method put the input of successive frames from the preprocessed magnetic resonance imaging (MRI) data into 3D ResNet18 with the classifier module for PD classification. Specifically, the attention mechanism is used to explore the discriminative features. Meanwhile, a self-supervised learning pretext task and a regression task are designed to assist in training and improve the robustness of the proposed model. We use a 5-fold cross-validation strategy to corroborate our method’s effectiveness on the Parkinson's Progression Markers Initiative (PPMI) dataset. The experimental results indicate that our proposed method has achieved an accuracy of 87.50% for PD classification, which outperforms the most state-of-the-art deep learning methods.

Parkinson’s disease (PD), a common and irreversible neurodegenerative progressive disease, brings huge pain and economic burden to the patients and their families in the late stage. As the disease is incurable, its early diagnosis and treatment are of paramount importance to ameliorate its deterioration. In this paper, we adopt an attention-based graph neural network (AGNN) for early diagnosis of PD via diffusion tensor imaging (DTI) data and phenotypic information. Firstly, we construct a structural brain connectivity network for every subject of DTI data. Secondly, we construct a graph based on phenotypic information and feature similarity from the brain connectivity network. Thirdly, we input the graph into AGNN to get the final classification and prediction results. Our method is validated on the public available Parkinson’s Progression Markers Initiative (PPMI) datasets. The results demonstrate that our adopted AGNN method is practical to classify and predict early PD deterioration and better for selected algorithms, with a mean accuracy of 96.48% in our early PD classification tasks.

Due to the discrepancy of different devices for fundus image collection, a well-trained neural network is usually unsuitable for another new dataset. To solve this problem, the unsupervised domain adaptation strategy attracts a lot of attentions. In this paper, we propose an unsupervised domain adaptation method based image synthesis and feature alignment (ISFA) method to segment optic disc and cup on fundus images. The GAN-based image synthesis (IS) mechanism along with the boundary information of optic disc and cup is utilized to generate target-like query images, which serves as the intermediate latent space between source domain and target domain images to alleviate the domain shift problem. Specifically, we use content and style feature alignment (CSFA) to ensure the feature consistency among source domain images, target-like query images and target domain images. The adversarial learning is used to extract domain-invariant features for output-level feature alignment (OLFA). To enhance the representation ability of domain-invariant boundary structure information, we introduce the edge attention module (EAM) for low-level feature maps. Eventually, we train our proposed method on the training set of the REFUGE challenge dataset and test it on Drishti-GS and RIM-ONE_r3 datasets. On the Drishti-GS dataset, our method achieves about 3% improvement of Dice on optic cup segmentation over the next best method. We comprehensively discuss the robustness of our method for small dataset domain adaptation. The experimental results also demonstrate the effectiveness of our method. Our code is available at https://github.com/thinkobj/ISFA.

Parkinson’s disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient’s motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the${l}_{\mathrm {2,p}}$norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson’s Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches.

Due to the discrepancy of different devices for fundus image collection, a well-trained neural network usually fails to be applied to another new dataset. To solve this problem, the unsupervised domain adaptation strategy attracts a lot of attention. In this paper, we adopt image synthesis and adversarial learning mechanism to complete input-level adaptation and output-level adaptation, respectively. In particular, the edge structure information of optic disc and cup is embedded into the devised encoder-decoder structure in feature-level adaptation to obtain domain-invariant features. To enhance the ability of feature representation, we introduce the position attention module and the edge attention module to extract discriminative features. We train our proposed method on the training set of the REFUGE challenge dataset and test it on Drishti-GS and RIM-ONE-r3 datasets. The experimental results demonstrate that our method is promising with respect to the segmentation of optic disc and cup.

Mitotic count is an important indicator for assessing the invasiveness of breast cancers. Currently, the number of mitoses is manually counted by pathologists, which is both tedious and time-consuming. To address this situation, we propose a fast and accurate method to automatically detect mitosis from the histopathological images. The proposed method can automatically identify mitotic candidates from histological sections for mitosis screening. Specifically, our method exploits deep convolutional neural networks to extract high-level features of mitosis to detect mitotic candidates. Then, we use spatial attention modules to re-encode mitotic features, which allows the model to learn more efficient features. Finally, we use multi-branch classification subnets to screen the mitosis. Compared to existing related methods in literature, our method obtains the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition. Code has been made available at: https://github.com/liushaomin/MitosisDetection.

Parkinson's disease (PD) is an irreversible neurodegenerative disease that seriously affects patients' lives. To provide patients with accurate treatment in time and to reduce deterioration of the disease, it is critical to have an early diagnosis of PD and accurate clinical score predictions. Different from previous studies on PD, most of which only focus on feature selection methods, we propose a network combining joint learning from multiple modalities and relations (JLMMR) with sparse nonnegative autoencoder (SNAE) to further enhance the ability of feature expression. We first preprocess and extract features of the modal neuroimaging data with multiple time points. To extract discriminative and informative features from longitudinal data, we apply JLMMR method for feature selection to avoid over-fitting issues. We further exploit SNAE to learn longitudinal discriminative features for joint disease diagnosis and obtain clinical score predictions. Extensive experiments on the publicly available Parkinson's Progression Markers Initiative (PPMI) dataset show the proposed method produces promising classification and prediction performance, which outperforms state-of-the-art methods as well.

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.

Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemical properties of protein (e.g., hydrophobicity and hydrophilicity). Deep polynomial network (DPN) is well-suited to integrate these modalities since it can represent any function on a finite sample dataset via the supervised deep learning algorithm. We propose a multimodal DPN (MDPN) algorithm to effectively integrate these modalities to enhance prediction performance. MDPN consists of a two-stage DPN, the first stage feeds multiple protein features into DPN encoding to obtain high-level feature representation while the second stage fuses and learns features by cascading three types of high-level features in the DPN encoding. We employ a regularized extreme learning machine to predict PPIs. The proposed method is tested on the public dataset of H. pylori, Human, and Yeast and achieves average accuracies of 97.87%, 99.90%, and 98.11%, respectively. The proposed method also achieves good accuracies on other datasets. Furthermore, we test our method on three kinds of PPI networks and obtain superior prediction results.

Neuroimaging techniques have been widely applied to various neurodegenerative disease analysis to reveal the intricate brain structure. The high dimensional neuroimaging features and limited sample size are the main challenges for the diagnosis task due to the unbalanced input data. To handle it, a sparse low-rank learning framework is proposed, which unveils the underlying relationships between input data and output targets by building a matrix-regularized feature network. Then we obtain the feature weight from the network based on local clustering coefficients. By discarding the irrelevant features and preserving the discriminative structured features, our proposed method can select the most relevant features and identify different stages of Parkinson's disease (PD) from normal controls. Extensive experimental results evaluated on the Parkinson's progression markers initiative (PPMI) dataset demonstrate that the proposed method achieves promising classification performance and outperforms the conventional algorithms. Furthermore, it can detect potential brain regions related to PD for future medical analysis.

To diagnose various autoimmune diseases, the accurate Human Epithelial-2 (HEp-2) cell image classification is a very important step. Automatic classification of HEp-2 cell using microscope image is a highly challenging task due to the strong illumination changes derived from the low contrast of the cells. To address this challenge, we propose a deep residual network (ResNet) based framework to recognize HEp-2 cell automatically. Specifically, a residual network of 50 layers (ResNet-50) with substantial deep layer is adopted to acquire the informative feature for accurate recognition. To further boost the recognition performance, we devise a novel ResNet-based network with deep supervision. The deeply supervised ResNet (DSRN) can address the optimization problem of gradient vanishing/exploding and accelerate the convergence speed. DSRN can directly guide the training of the lower and upper levels of the network to counteract the effects of unstable gradient variations by the adverse training process. As a result, DSRN can extract more discriminative features. Experimental results show that our proposed DSRN method can achieve an average classification accuracy of 93.46% and 95.88% on ICPR20l2 and ICPR20l6- Taskl datasets, respectively. Our proposed method outperforms the traditional methods as well.

Accurate Human Epithelial-2 (HEp-2) cell image classification plays an important role in the diagnosis of many autoimmune diseases. However, the traditional approach requires experienced experts to artificially identify cell patterns, which extremely increases the workload and suffer from the subjective opinion of physician. To address it, we propose a very deep residual network (ResNet) based framework to automatically recognize HEp-2 cell via cross-modal transfer learning strategy. We adopt a residual network of 50 layers (ResNet-50) that are substantially deep to acquire rich and discriminative feature. Compared with typical convolutional network, the main characteristic of residual network lie in the introduction of residual connection, which can solve the degradation problem effectively. Also, we use a cross-modal transfer learning strategy by pre-training the model from a very similar dataset (from ICPR2012 to ICPR2016-Task1). Our proposed framework achieves an average class accuracy of 95.63% on ICPR2012 HEp-2 dataset and a mean class accuracy of 96.87% on ICPR2016-Task1 HEp-2 dataset, which outperforms the traditional methods.

This paper proposed an adaptive sparse learning (ASL) framework to solve the multi-classification problem for neurodegenerative disease analysis. Specifically, we integrate the idea of feature selection and subspace learning to construct a least square regression model. The principle of Fisher's linear discriminant analysis (LDA) and locality preserving projection (LPP) are incorporated to utilize the global and local information in the original data space. Additionally, we introduce a generalized norm to the loss function to regulate the sparseness degree. This framework can select the most relative and distinguishable features to enhance classification performance. Unlike most previous methods for binary classification, we perform a multiclassification to improve the efficiency of computer-aided diagnosis. Our proposed method is validated on the public available Parkinson's progression markers initiative (PPMI) and Alzheimer's disease neuroimaging initiative (ADNI) datasets. Experimental results show that our proposed method can identify subjects more accurately compared to other state-of-the-art methods.

Though there are many computational models proposed for saliency detection, few of them take object boundary information into account. This paper presents a hierarchical saliency detection model incorporating probabilistic object boundaries, which is based on the observation that salient objects are generally surrounded by explicit boundaries and show contrast with their surroundings. We perform adaptive thresholding operation on ultrametric contour map, which leads to hierarchical image segmentations, and compute the saliency map for each layer based on the proposed robust center bias, border bias, color dissimilarity and spatial coherence measures. After a linear weighted combination of multi-layer saliency maps, and Bayesian enhancement procedure, the final saliency map is obtained. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed model outperforms eight state-of-the-art saliency detection models.

In this paper, a new method is proposed to incorporate the saliency map to weight the extracted features with discriminative technique for learning the spatial discriminative information of images. Different from the conventional bag of word (BoW) approach, the descriptive bag of phrase approach is explored to capture the word co-occurrence and dependence. The image score based on the saliency map is learned to optimize the support vector machine (SVM) parameter. Discriminative learning techniques are adopted based on image score and fed into the SVM classifier. Moreover, the histogram intersection mapping and normalization method is further adopted to enhance the classification performance. Experimental results on the 3 popular databases demonstrate the effectiveness of the method and show the promising performance over the existing state-of-the-art methods.