Catastrophic forgetting remains a fundamental barrier to artificial continual learning (CL) - a capability innate to humans. Existing CL methods often incur prohibitive computational costs in resource-constrained scenarios. Spiking neural networks (SNNs), with their biological plausibility and energy efficiency, offer distinct advantages for CL. Inspired by cortico-hippocampal memory mechanisms, we propose a spiking neural network framework integrating Hebbian plasticity with meta-learning, named HLML-SNN. This architecture emulates a dual-phase CL process: (1) In the short-term phase, sample-level Hebbian learning rapidly adapts to new inputs through local synaptic updates; (2) In the long-term phase, task-level meta-learning optimizes cross-task parameters using consolidated synaptic weights, mimicking cortical memory integration to refine shared representations and initialize subsequent Hebbian learning. HLML-SNN incrementally transforms short-term adaptations into stable long-term knowledge, where the synergy of rapid synaptic updates and meta-driven global optimization enables efficient continual learning while balancing stability and plasticity. Empirical results establish HLML-SNN's state-of-the-art performance across split-MNIST/CIFAR10/CIFAR100/TinyImageNet while markedly reducing training time compared to existing methods, demonstrating substantial practical potential for rapid deployment scenarios. The code and appendix are available on https://github.com/JiangshuaiXu/HLML SNN.

Diabetic retinopathy (DR) is the prominent cause of permanent blindness, which brings the risk of visual damage to the all-age population. Accurate grading of DR severity aids doctors in diagnosing and treating this condition. In essence, DR grading is an implicit two-stage process. The first stage aims to detect suspicious lesion regions, and the second stage integrates lesion information to obtain DR grading results. However, the lack of pixel-level lesion annotations hinders the detection of suspicious lesions and impacts the performance of the automatic DR disease diagnosis system. Therefore, how to obtain reliable lesion features without pixel-level lesion annotations is essential for predicting the DR grading results. In this work, we introduce the prompt paradigm to DR grading and propose a hybrid CNN-Transformer framework named WSL-DR, which utilizes weakly-supervised lesion detection to obtain suspicious lesion prompt information for guiding DR grading. Furthermore, to mitigate the semantic gap, we design a prompt-based receptive channel field (PRCF) encoder to effectively fuse local lesion prompt information with global image information. Extensive experimental results have shown that our method outperforms state-of-the-art DR grading methods utilizing only image-level labels on the EyePACS and Messidor benchmark datasets.

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.

Abstract Fires not only cause devastating consequences for human life and property, but also lead to soil erosion in forests. Therefore, it is necessary to design a novel algorithm that can quickly monitor smoke from fires. Most existing smoke segmentation methods do not consider the segmentation accuracy of algorithms under limited computational resources. To address this research gap, this paper proposes a lightweight smoke segmentation algorithm based on DeepLabV3+ that achieves fast inference speed and high accuracy for different sizes smoke. To reduce the number of parameters, the feature extraction network of the DeeplabV3+ algorithm is replaced by MobileNetV2, which enhances the extraction ability of the algorithm in segment smoke images. Then, the Convolutional Block Attention Module (CBAM) is added to the encoder part to enhance the perception of the algorithm for small smoke and effectively alleviates smoke mis‐segmentation. Furthermore, a newly designed loss function is used in the network. The experimental results show that the proposed method has improved by 1.27% in Smoke IoU and 1.21% in mPA compared with other methods. The weight size has been reduced to 10.76% of the original DeepLabV3+, and the inference time is only 33.71ms. Therefore, it is a more suitable early fire detection algorithm for resource‐constrained environments.

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.

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.

Diabetic retinopathy (DR) is one of the most serious complications of diabetes and is a prominent cause of permanent blindness. However, the low-quality fundus images increase the uncertainty of clinical diagnosis, resulting in a significant decrease on the grading performance of the fundus images. Therefore, enhancing the image quality is essential for predicting the grade level in DR diagnosis. In essence, we are faced with three challenges: (I) How to appropriately evaluate the quality of fundus images; (II) How to effectively enhance low-quality fundus images for providing reliable fundus images to ophthalmologists or automated analysis systems; (III) How to jointly train the quality assessment and enhancement for improving the DR grading performance. Considering the importance of image quality assessment and enhancement for DR grading, we propose a collaborative learning framework to jointly train the subnetworks of the image quality assessment as well as enhancement, and DR disease grading in a unified framework. The key contribution of the proposed framework lies in modelling the potential correlation of these tasks and the joint training of these subnetworks, which significantly improves the fundus image quality and DR grading performance. Our framework is a general learning model, which may be useful in other medical images with low-quality data. Extensive experimental results have shown that our method outperforms state-of-the-art DR grading methods by a considerable 73.6% ACC/71.2% Kappa and 88.5% ACC/86.3% Kappa on Messidor and EyeQ benchmark datasets, respectively. In addition, our method significantly enhances the low-quality fundus images while preserving fundus structure features and lesion information. To make the framework more general, we also evaluate the enhancement results in more downstream tasks, such as vessel segmentation.