The progression of medical image analysis methodologies has significantly assisted fundus clinical decision-making, such as disease diagnosis and lesion segmentation. However, low-quality fundus images bring a series of challenges to the automatic screening of diseases and the segmentation of lesions. Most existing methods primarily concentrate on enhancing image quality by utilizing the supervision of paired fundus images, which are difficult to collect in real medical applications. High-quality reference images are essential for guiding quality enhancement. To this end, we propose an enhancement method for low-quality fundus images, called RF-IQE, to alleviate the requirement for paired training images and only requires low-quality fundus images. Specifically, we first construct the patch-level high-/low-quality domains by employing a rule-based quality assessment scheme. Then, to achieve the fundus image quality enhancement and unified illumination styles simultaneously, we formulate them as a patch quality domain adaptation and a multi-style domain adaptation, respectively. We qualitatively and quantitatively demonstrate that our reference-free image quality enhancement network outperforms the conventional methods and exhibits comparable performance than the deep learning-based image enhancement methods with paired images on both the EyeQ and Messidor datasets. Furthermore, we also investigate the influence of the RF-IQE method on various fundus imaging analysis tasks, including vessel segmentation, optic disc segmentation, lesion segmentation, and disease classification.

Medical images acquired under suboptimal conditions often suffer from quality degradation, such as low-light, blurring, and artifacts. Such degradations obscure the lesions and anatomical structures in medical images, making it difficult to distinguish key pathological regions. This significantly increases the risk of misdiagnosis by automated medical diagnostic systems or clinicians. To address this challenge, we propose a multi-Color space-based quality enhancement network (MSQNet) that effectively eliminates global low-quality factors while preserving pathology-related characteristics for improved clinical observation and analysis. We first revisit the properties of image quality enhancement in different color spaces, where the V-channel in the HSV space can better represent the contrast and brightness enhancement process, whereas the A/B-channel in the LAB space is more focused on the color change of low-quality images. The proposed framework harnesses the unique properties of different color spaces to optimize the image enhancement process. Specifically, we propose a pathology-preserving transformer, designed to selectively aggregate features across different color spaces and enable comprehensive multiscale feature fusion. Leveraging these capabilities, MSQNet effectively enhances low-quality RGB medical images while preserving key pathological features, thereby establishing a new paradigm in medical image enhancement. Extensive experiments on three public medical image datasets demonstrate that MSQNet outperforms traditional enhancement techniques and state-of-the-art methods, in terms of both quantitative metrics and qualitative visual assessment. MSQNet successfully improves image quality while preserving pathological features and anatomical structures, facilitating accurate diagnosis and analysis by medical professionals and automated systems.

Internet of Things (IoT) technology enables enhanced connectivity and information sharing among various devices and platforms. In the context of vehicular crowds ensing, this connectivity has opened up new way to collect environmental data via Vehicle-based Visual Crowdsensing. However, the heterogeneity of data sources and the presence of vehicle outliers pose challenges of ensuring the reliability and accuracy of the machine learning (ML) models. We propose GeoFed, a geography-aware federated learning (FL) approach for vehicular visual crowdsensing. Here, geographically similar vehicular fog nodes (VFNs) collaborate to train a cluster model unlike the traditional FL approaches where vehicles participate to train a model. To further improve GeoFed's performance, we employ the deep Q-Network (DQN) algorithm to intelligently determine the participation of vehicles in the FL process. Through extensive experiments on our own collected real-world dataset, we find that our proposed GeoFed not only outperforms the state-of-art FedAvg with higher F1 score (1.18 x) and mAP (1.14 x), but also achieves a faster convergence rate with less loss (80%).

Medical image analysis techniques have been employed in diagnosing and screening clinical diseases. However, both poor medical image quality and illumination style inconsistency increase uncertainty in clinical decision-making, potentially resulting in clinician misdiagnosis. The majority of current image enhancement methods primarily concentrate on enhancing medical image quality by leveraging high-quality reference images, which are challenging to collect in clinical applications. In this study, we address image quality enhancement within a fully self-supervised learning setting, wherein neither high-quality images nor paired images are required. To achieve this goal, we investigate the potential of self-supervised learning combined with domain adaptation to enhance the quality of medical images without the guidance of high-quality medical images. We design a Domain Adaptation Self-supervised Quality Enhancement framework, called DASQE. More specifically, we establish multiple domains at the patch level through a designed rule-based quality assessment scheme and style clustering. To achieve image quality enhancement and maintain style consistency, we formulate the image quality enhancement as a collaborative self-supervised domain adaptation task for disentangling the low-quality factors, medical image content, and illumination style characteristics by exploring intrinsic supervision in the low-quality medical images. Finally, we perform extensive experiments on six benchmark datasets of medical images, and the experimental results demonstrate that DASQE attains state-of-the-art performance. Furthermore, we explore the impact of the proposed method on various clinical tasks, such as retinal fundus vessel/lesion segmentation, nerve fiber segmentation, polyp segmentation, skin lesion segmentation, and disease classification. The results demonstrate that DASQE is advantageous for diverse downstream image analysis tasks.

Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-confidence pseudo labels from the perturbed inputs during training. To address these issues, we propose an Uncertainty-guided Collaborative Mean-Teacher (UCMT) for semi-supervised semantic segmentation with the high-confidence pseudo labels. Concretely, UCMT consists of two main components: 1) collaborative mean-teacher (CMT) for encouraging model disagreement and performing co-training between the sub-networks, and 2) uncertainty-guided region mix (UMIX) for manipulating the input images according to the uncertainty maps of CMT and facilitating CMT to produce high-confidence pseudo labels. Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation. Extensive experiments on four public medical image datasets including 2D and 3D modalities demonstrate the superiority of UCMT over the state-of-the-art. Code is available at: https://github.com/Senyh/UCMT.

Retinal fundus images have been applied for the diagnosis and screening of eye diseases, such as Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). However, both low-quality fundus images and style inconsistency potentially increase uncertainty in the diagnosis of fundus disease and even lead to misdiagnosis by ophthalmologists. Most of the existing fundus image enhancement methods mainly focus on improving the image quality by leveraging the guidance of high-quality images, which is difficult to be collected in medical applications. In this paper, we tackle image quality enhancement in a fully unsupervised setting, i.e., neither paired images nor high-quality images. To this end, we explore the potential of the self-supervised task for improving the quality of fundus images without the requirement of high-quality reference images, and proposed a Domain Adaptation Self-supervised Quality Enhancement framework, named DASQE. Specifically, we construct multiple patch-wise domains via a well-designed rule-based quality assessment scheme and style clustering. To achieve robust low-quality image enhancement and address style inconsistency, we formulate two self-supervised domain adaptation tasks to disentangle the features of image content, low-quality factors and style information by exploring intrinsic supervision signals within the low-quality images. Extensive experiments are conducted on four benchmark datasets, and results show that our DASQE method achieves new state-of-the-art performance when only low-quality images are available.

How to automatically predict people’s gaze has attracted attention in the field of computer vision and machine learning. Previous studies on this topic set many constraints, such as restricted scenarios and strict and complex inputs. To mitigate these constraints to predict the gaze of people in more general scenarios, we propose a three-pathway network (TPNet) to estimate gaze via the joint modeling of multiple cues. Specifically, we first design a human-centric relationship inference (HCRI) module to learn the object-level relationship between the target person and the surrounding persons/objects in a scene. To the best of our knowledge, this is the first time that the object-level relationship is introduced into the gaze estimation task. Then, we construct a novel deep network with three pathways to fuse multiple cues, including scene saliency, object-level relationships and head information, to predict the gaze target. In addition, to extract the multilevel features during network training, we build and embed a micropyramid module in TPNet. The performance of TPNet is evaluated on two gaze estimation datasets: GazeFollow and DLGaze. A large number of quantitative and qualitative experimental results verify that TPNet can obtain robust results and significantly outperform the existing state-of-the-art gaze estimation methods. The code of TPNet will be released later.

Modeling spatio-temporal dynamics in functional brain networks is critical for underlying the functional mechanism of autism spectrum disorder (ASD). In our study, we propose an end-to-end framework called temporal graph representation learning for brain networks, which thoroughly captures spatio-temporal features in resting-state functional magnetic resonance imaging (rs-fMRI) data. Specifically, we first transform rs-fMRI time-series into temporal multi-graph using a sliding window technique. A temporal multi-graph clustering is then designed to eliminate the inconsistency of the temporal multi-graph series. Then, a graph structure aware LSTM (GSA-LSTM) is proposed to capture the spatio-temporal embedding for temporal graphs. The proposed GSA-LSTM can not only capture discriminative features for prediction but also impute the incomplete graphs for the temporal multi-graph series. Extensive experiments on autism brain imaging data exchange (ABIDE) dataset shows the effectiveness of our proposed framework. The results demonstrate that the proposed dynamic brain network embedding learning outperforms the state of-the-art brain network classification models. Furthermore, the obtained clustering results are consistent with the previous neuroimaging-derived evidence of biomarkers for autism spectrum disorder (ASD).

Alzheimer’s disease (AD), the most common type of dementia, not only imposes a huge financial burden on the health care system, but also a psychological and emotional burden on patients and their families. There is thus an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. In this article, we propose the multi-task learning with fused Laplacian sparse group lasso model, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the proposed model is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.