Immunohistochemistry (IHC) staining is crucial for determining tumor subtypes, obtaining protein expression information, and developing personalized treatment plans. But compared with hematoxylin and eosin (H&E) staining, IHC staining is more complex and expensive. With the advancement of deep learning, converting H&E stained images into IHC stained images has gradually emerged as a solution for obtaining IHC staining. However, current virtual staining processes suffer from difficulties in aligning pathological semantic features, posing significant challenges for network training, which poses significant challenges for network training. To solve these issues, we propose a multi-view multi-level pathology semantic perception learning method for H&E-to-IHC virtual staining (M2PL-GAN). Unlike prior approaches, M2PL-GAN introduces a comprehensive semantic learning paradigm from three views: structural contextual relations, feature distribution, and topology-aware fine-grained semantics. These correspond to the Context-aware Correlation Mechanism (CACM), the Local-aware Distribution Alignment Mechanism (LDAM), and the Graph- aware Bidirectional Contrastive Learning Mechanism (GBCLM) respectively. Among them, CACM enhances contextual consistency by establishing semantic correlations between virtual and real IHC images at local scales. LDAM ensures alignment of semantic feature distributions between virtual and real IHC images, mitigating semantic shifts caused by HE-IHC staining. GBCLM leverages graph neural network to capture topology-aware semantic representations and optimizes semantic feature alignment through bidirectional contrastive learning. Extensive experiments on both public and private datasets demonstrate that our method outperforms state-of-the-art approaches in both quantitative metrics and qualitative evaluations. Our code is available in https://github.com/Pikachu-one/M2PL-GAN.

As an indispensable part of geophysical exploration, seismic inversion can obtain the properties of subsurface media based on seismic data and available well-log information. With the nonlinear mapping ability, deep neural networks can map seismic data to well-log of interest. Interpreting gamma is crucial as it is essential for determining lithology and indicating sediment characteristics. Stratigraphic frameworks can approximate low-frequency trends in subsurface properties and are often used to guide well-log interpolation effectively. However, the existing deep neural network models cannot effectively explicitly fuse critical stratigraphic information, which will restrict the physical explainability and correctness of the seismic inversion. Thus, we propose a stratigraphic-encoded transformer algorithm, named SeisWellTrans, to build a gamma log inversion model using horizon position encoding and seismic trace as inputs. Specifically, the incorporation of stratigraphic information from several horizons is crucial for improving the resolution of the output; and SeisWellTrans can efficiently model context in seismic sequences by capturing the interactions between horizon position encodings. We take the Volve field data as an example and use several gamma curves as training labels, and numerical experiments demonstrate the geologically reasonable performance and high validation accuracy of this network and the crucial role that stratigraphic information plays. On the four validation wells, stratigraphic-encoded SeisWellTrans obtained an average correlation coefficient of 86%, exceeding 79% of stratigraphic-encoded convolutional neural network (CNN).

In remote sensing image analyses, the extraction of lake water bodies has been emphasized owing to its pivotal role in interpreting aquatic ecosystems, assessing hydrological trends, and detecting environmental changes. Although deep-learning-based techniques have been effectively deployed for this task, several challenges persist, including mis-segmentation of low-contrast regions, insufficient delineation of fuzzy boundaries, and over-segmentation in micro-regions. To address these issues, an innovative, end-to-end segmentation framework is proposed in this study. This framework ingeniously integrates the Geodesic Active Contour (GAC) model with the Vision Transformer (ViT) architecture, thereby offering a robust solution for fain-gained lake water body extraction. Specifically, the conventional GAC model is first revisited and reformulated. Subsequently, the Differentiable Spectral Clustering (DSC) module is designed for automatic contour initialization and lifts the dependence on hard threshold binarization. Finally, an iterative learning strategy is introduced to integrate the aforementioned models within the ViT architecture. This strategy serves to constrain the geometric characteristics, while also permitting dynamic adjustment of evolution parameters, facilitating enhanced refinement of spatial details and accuracy in segmentation results. Comprehensive experiments conducted on the newly constructed Global Lake View (GLV) dataset and a public benchmark demonstrated that the proposed method yields precise lake boundaries and achieves state-of-the-art performance.

With the rapid development of the aviation industry, videoscope inspection of aeroengines has become crucial for ensuring aircraft flight safety. Recently, deep learning, particularly convolutional neural networks (CNNs), have shown remarkable efficacy in videoscope inspection tasks. However, these methods usually require large-scale training labels with accurate annotation. In videoscope images, defects are often present at the micrometer level and require manual labeling by professional inspection personnel, posing further challenges to model training. To address these issues, weakly supervised deep level set (WDLS), a fully automatic framework for aeroengine defect segmentation, was proposed in this work. WDLS employs a multibranch structure and an iterative learning strategy to combine a high-performance CNN architecture with level set evolution. First, the similarity-guided region detector was designed to generate class-specific segmentation proposals and initialize the level set function. Second, the adaptive local parameter prediction algorithm was proposed to integrate local priors and constraints into the energy function and optimize the iterative process. Finally, a self-supervised objective function named convexified level set was introduced to represent an improved level set formulation and obtain the final segmentation. The proposed framework is continuously differentiable and unified. Thus, it can be seamlessly embedded in any CNN without postprocessing. Furthermore, the method was evaluated on a new aeroengine dataset Turbo19 and a benchmark dataset. Experimental results demonstrated that the proposed framework meets real-time requirements and achieves better performance than state-of-the-art methods.

In aeroengine maintenance, endoscopic imaging serves as a crucial tool for detecting blade defects and evolves toward intelligence driven by computer vision technology. Currently, supervised-learning-based defect segmentation methods mainly rely on extensive pixel-level annotations, making it laborious and time consuming. This article shifts focus to the abundant unlabeled data in real-world scenarios and introduces an innovative semisupervised defect segmentation method termed SAIT. Within this framework, three parallel self-supervised mechanisms are adeptly integrated with a semisupervised framework, aiming to bolster defect semantic segmentation with limited labeled samples. In the initial phase, by leveraging the capability of the vision transformer to dissect images into patches, four stochastic distortions are seamlessly infused into the patch sequence. Subsequently, three self-supervised tasks from image level to pixel level are achieved through a customized joint objective function paired with a tailored backbone network. In the second phase, SAIT undergoes pixel-level fine-tuning via the proposed class-centric loss, mitigating class imbalances in limited sample sizes and enhancing initial training. Experiments on a proprietary dataset demonstrate that SAIT achieved 78.42% and 86.70% in mean intersection over union and mean pixel accuracy metrics, respectively, with 25% labeled data, significantly improving the performance of existing semisupervised defect segmentation techniques. Meanwhile, experiments on the open-source dataset further indicate that SAIT holds promise for application in other industrial sectors beyond aeroengine inspection.

This study addresses the fine-grained segmentation of defects in bridge pavements, which is crucial for the maintenance and structural safety of bridges. Although bridge pavements pose distinctive challenges owing to their unique characteristics and varied defect types, previous studies have primarily focused on the detection of slender cracks. To fill this research gap, we developed a novel end-to-end hybrid method that dynamically combines the vision transformer (ViT) and level set theory to handle the complex geometry of bridge pavement defects. The novelty of the proposed method lies in the configuration of two parallel decoders. These decoders, operating under a unified objective function, share weights and perform simultaneous optimization, thereby facilitating a holistic end-to-end training process. Furthermore, we compiled two new bridge pavement defect datasets, namely BdridgeDefX and BdridgeDef20, which offer broader applicability for practical defect detection. The results of a rigorous experimental validation on four datasets demonstrated the proposed method’s capability of generating accurate defect boundaries and delivering state-of-the-art performance.

Steganography is an important technology for ensuring the security of cyberspace and the privacy of communications. In the last decade, emerging biotechnology has made it possible for DNA to be used as a promising steganographic carrier with high hidden capacity, high imperceptibility and high feasibility. However, severe statistical distortion might appear in steganographic carriers generated by existing DNA steganographies when they are compared with the natural ones. Therefore, efforts are being made to seek an advanced strategy to generate quasi-natural steganographic carriers with a strong anti-steganalysis capability. In this work, we first thoroughly analyze and model the numerous complicated statistical properties that exist in natural DNA chains, and then utilize the LSTM model to learn the serialized statistical properties. After obtaining an optimal sequence model that highly satisfies the statistical properties of natural DNA chains, we utilize the Adaptive Dynamic Grouping (ADG) algorithm to perform information hiding. In addition, we have carried out experimental analysis and verification from the perspectives of perceptual-imperceptibility, statistical-imperceptibility, and anti-steganalysis capability, all of which show that our proposed steganography method vastly outperforms previous DNA steganographic methods, taking a successful step towards achieving higher security DNA steganography.

Under the requirement of highly reliable encryption, the design of true random number generators (TRNGs) based on field-programmable gate arrays (FPGAs) is receiving increased attention. Although TRNGs based on ring oscillators (ROs) and phase-locked loops (PLLs) have the advantages of small resource overhead and high throughput, there are problems such as instability of randomness and poor portability. To improve the randomness, portability, and throughput of a random number generator, we design a TRNG whose randomness is generated by the oscillation of self-timed rings (STRs) and accurately extracted by a jitter-latch structure. The portability of the structure is verified by electronic design automation (EDA) tools. Under the condition of 0°C-80°C ambient temperature and 1.0 ± 0.1 V output voltage, the proposed structure is tested many times on Xilinx Spartan-6 and Virtex-6 FPGAs with an automatic routing mode. Theoretical analysis shows that this method can effectively improve the coverage of jitter and reduce the migration phenomenon. Experimental results show excellent performance in randomness, robustness, and portability, and the throughput reaches 100 Mbps.