Parallel guided local-overall workflow segmentation network for cloud and snow segmentation

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

Network security is critical for organizations to secure their network resources from intrusion and attacks. A security policy is a rule enforced in the network to allow or block network traffic. To write security policies, network analysts divide their networks into segments or parts with similar security needs. Segmentation makes writing security policies manageable and identifies robust security policies for the network. Visualizations can help analysts to understand the segmented network and define security policies. We contribute Portola, a hybrid tree and network visualization technique to display a segmented computer network. Portola presents an overview of the segmentation as a hierarchy and displays connections within the network. Using Portola, analysts can explore a segmented network, identify nodes and connections of interest through exploratory network analysis, and drill down on elements of interest to reason about the patterns of relationships in the network. Through this work, we also discuss the goals of network analysts who work with segmented networks and discuss the lessons learned from the user-centered iterative design of Portola.

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

Training a fully supervised semantic segmentation network requires a large amount of expensive pixel-level annotations in manual labor. In this work, we focus on studying the semantic segmentation problem using only image-level supervision. An effective scheme for weakly supervised segmentation is employed to produce the proxy annotations via image tags firstly. Then the segmentation network is retrained on the generated noisy proxy annotations. However, learning from noisy annotations is risky, as proxy annotations of poor quality may deteriorate the performance of the baseline segmentation and classification networks. In order to train the segmentation network using noisy annotations more effectively, two novel loss functions are proposed in this paper, namely, the selection loss and attention loss. Firstly, a selection loss is designed by weighting the proxy annotations based on a coarse-to-fine strategy for evaluating the quality of segmentation masks. Secondly, an attention loss taking the clean image tags as supervision is utilized to correct the classification errors caused by ambiguous pixel-level labels. Finally, we propose an end-to-end semantic segmentation network SAL-Net guided by the above two losses. From the extensive experiments conducted on PASCAL VOC 2012 dataset, SAL-Net reaches state-of-the-art performance with mean IoU (mIoU) as 62.5% and 66.6% on the test set by taking VGG16 network and ResNet101 network as the baselines respectively, which demonstrates the superiority of the proposed algorithm over eight representative weakly supervised segmentation methods. The code and models are available at https://github.com/zmbhou/SALTMM.

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy.

Adversarial consistency-based semi-supervised pneumonia segmentation using dual multiscale feature selection and fusion mean teacher model and triple-attention dynamic convolution in chest CTs.

In recent years, deep learning has achieved good results in the semantic segmentation of medical images. A typical architecture for segmentation networks is an encoder-decoder structure. However, the design of the segmentation networks is fragmented and lacks a mathematical explanation. Consequently, segmentation networks are inefficient and less generalizable across different organs. To solve these problems, we reconstructed the segmentation network based on mathematical methods. We introduced the dynamical systems view into semantic segmentation and proposed a novel segmentation network based on Runge-Kutta methods, referred to hereafter as the Runge-Kutta segmentation network (RKSeg). RKSegs were evaluated on ten organ image datasets from the Medical Segmentation Decathlon. The experimental results show that RKSegs far outperform other segmentation networks. RKSegs use few parameters and short inference time, yet they can achieve competitive or even better segmentation results compared to other models. RKSegs pioneer a new architectural design pattern for segmentation networks.

Network segmentation is a critical cybersecurity strategy that involves dividing a network into smaller, isolated segments or subnetworks to enhance security and improve network performance. By limiting access to sensitive data and systems, network segmentation reduces the attack surface and prevents lateralmovement by malicious actors within an enterprise network. This research article examines the role of network segmentation as a defense mechanism in securing enterprise networks. It explores the methodologies, benefits, and challenges associated with implementing network segmentation. The study employs a mixed-methods approach, including a comprehensive literature review and analysis of real-world case studies, to assess the effectiveness of network segmentation in mitigating cyber threats. The findings highlight that while network segmentation significantly enhances security posture by containing breaches and restricting unauthorized access, it also presents challenges such as increased complexity and management overhead. The paper concludes with recommendations for best practices in implementing network segmentation to bolster enterprise security

There are few studies utilizing only IR cameras for long-distance gender recognition, and they have shown low recognition performance due to their lack of color and texture information in IR images with a complex background. Therefore, a rough body segmentation-based gender recognition network (RBSG-Net) is proposed, with enhanced gender recognition performance achieved by emphasizing the silhouette of a person through a body segmentation network. Anthropometric loss for the segmentation network and an adaptive body attention module are also proposed, which effectively integrate the segmentation and classification networks. To enhance the analytic capabilities of the proposed framework, fractal dimension estimation was introduced into the system to gain insights into the complexity and irregularity of the body region, thereby predicting the accuracy of body segmentation. For experiments, near-infrared images from the Sun Yat-sen University multiple modality re-identification version 1 (SYSU-MM01) dataset and thermal images from the Dongguk body-based gender version 2 (DBGender-DB2) database were used. The equal error rates of gender recognition by the proposed model were 4.320% and 8.303% for these two databases, respectively, surpassing state-of-the-art methods.

Sea–land segmentation and ship detection are two prevalent research domains for optical remote sensing harbor images and can find many applications in harbor supervision and management. As the spatial resolution of imaging technology improves, traditional methods struggle to perform well due to the complicated appearance and background distributions. In this paper, we unify the above two tasks into a single framework and apply the deep convolutional neural networks to predict pixelwise label for an input. Specifically, an edge aware convolutional network is proposed to parse a remote sensing harbor image into three typical objects, e.g., sea, land, and ship. Two innovations are made on top of the deep structure. First, we design a multitask model by simultaneously training the segmentation and edge detection networks. Hierarchical semantic features from the segmentation network are extracted to learn the edge network. Second, the outputs of edge pipeline are further employed to refine entire model by adding an edge aware regularization, which helps our method to yield very desirable results that are spatially consistent and well boundary located. It also benefits the segmentation of docked ships that are quite challenging for many previous methods. Experimental results on two datasets collected from Google Earth have demonstrated the effectiveness of our approach both in quantitative and qualitative performance compared with state-of-the-art methods.

M3U-CDVAE: Lightweight retinal vessel segmentation and refinement network

Synthetic Aperture Radar (SAR) is a high-resolution imaging sensor commonly mounted on platforms such as airplanes and satellites for widespread use. In complex electromagnetic environments, radio frequency interference (RFI) severely degrades the quality of SAR images due to its widely varying bandwidth and numerous unknown emission sources. Although traditional deep learning-based methods have achieved remarkable results by directly processing SAR images as visual ones, there is still considerable room for improvement in their performance due to the wide coverage and high intensity of RFI. To address these issues, this paper proposes the fusion of segmentation and inpainting networks (FuSINet) to suppress SAR RFI in the time-frequency domain. Firstly, to weaken the dominance of RFI in SAR images caused by high-intensity interference, a simple CCN-based network is employed to learn and segment the RFI. This results in the removal of most of the original interference, leaving blanks that allow the targets to regain dominance in the overall image. Secondly, considering the wide coverage characteristic of RFI, a U-former network with global information capture capabilities is utilized to learn the content covered by the interference and fill in the blanks created by the segmentation network. Compared to the traditional Transformer, this paper enhances its global information capture capabilities through shift-windows and down-sampling layers. Finally, the segmentation and inpainting networks are fused together through a weighted parameter for joint training. This not only accelerates the learning speed but also enables better coordination between the two networks, leading to improved RFI suppression performance. Extensive experimental results demonstrate the substantial performance enhancement of the proposed FuSINet. Compared to the PISNet+, the proposed attention mechanism achieves a 2.49 dB improvement in peak signal-to-noise ratio (PSNR). Furthermore, compared to Uformer, the FuSINet achieves an additional 4.16 dB improvement in PSNR.

In this paper, we propose an approach that improves segmentation networks with automatic augmentation networks for dental mesh data. Since conventional data augmentation is to augment all samples uniformly with predefined parameters, it ignores the unique characteristics of a single tooth sample and cannot make good use of the data set. And the traditional method separates data augmentation and segmentation network training, so the augmented data cannot be well adapted to the network to make it play a good role. We adopt a joint optimization strategy to integrate the augmentation network and the segmentation network, so that the augmented tooth data is the most suitable for the segmentation network. In addition, we design new improved loss functions suitable for augmentation and segmentation networks. Experiments have shown that the automatic augmentation network in our proposed method, named MeshAugNet, can effectively improve the segmentation accuracy after it is used for tooth segmentation. In general, this work achieves a combination of 3D dental data auto- augmentation network and segmentation network, which improves the accuracy of tooth segmentation, and can be used to solve the problem of too few samples in tooth datasets.

Employing representations generated by large-scale training in a transfer-learning setting achieves state-of-the-art anomaly segmentation results when applied to the visual inspection task. Current approaches, however, focus exclusively on features of pre-trained classification networks, which are known to posess lower spatial resolution than segmentation or object detection networks. In our work, we investigate whether features extracted from pre-trained segmentation networks can be used to further improve anomaly segmentation performance in the transfer-learning setting. To this end, we apply state-of-the-art transfer-learning methods to encoder-decoder based segmentation networks. Results show that the encoders of pre-trained segmentation networks yield improved anomaly segmentation performance compared to their pre-trained classification counterparts. However, no consistent improvements can be observed yet regarding the decoders of the pre-trained segmentation networks. Together, this demonstrates that pre-trained segmentation networks can be used to further improve transfer-learned anomaly segmentation performance and that additional research is required to fully unleash their potential.

Researchers have long argued that a “right” degree of closeness among team members is necessary for innovation. At unhealthy extremes, while closeness leads to cloning and copycat attitude, increased distance can result in incompatibility and dissonance. Hence, actually building teams that possess “creative-tension” is easier said than done. This chapter develops specific factors that conceptualize an “optimum” distance (vis-à-vis closeness) in teams and later extends the factors to argue for a novel organizational form, the “segmented network.”