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Background: Immunotherapies effectively treat human malignancies, but the low response and resistance are major obstacles. Neoantigen is an emerging target for tumor immunotherapy that can enhance anti-tumor immunity and improve immunotherapy. Aberrant alternative splicing is an important source of neoantigens. HNRNPA1, an RNA splicing factor, was found to be upregulated in the majority of tumors and play an important role in the tumor immunosuppressive microenvironment. Methods: Whole transcriptome sequencing was performed on shHNRNPA1 SKOV3 cells and transcriptomic data of shHNRNPA1 HepG2, MCF-7M, K562, and B-LL cells were downloaded from the GEO database. Enrichment analysis was performed to elucidate the mechanisms underlying the activation of anti-tumor immunity induced by HNRNPA1 knockdown. mRNA alternative splicing was analyzed and neoantigens were predicted by JCAST v.0.3.5 and Immune epitope database. The immunogenicity of candidate neoantigens was calculated by Class I pMHC Immunogenicity and validated by the IFN-γ ELISpot assay. The effect of shHNRNPA1 on tumor growth and immune cells in vivo was evaluated by xenograft model combined with immunohistochemistry. Results: HNRNPA1 was upregulated in a majority of malignancies and correlated with immunosuppressive status of the tumor immune microenvironment. Downregulation of HNRNPA1 could induce the activation of immune-related pathways and biological processes. Disruption of HNRNPA1 resulted in aberrant alternative splicing events and generation of immunogenic neoantigens. Downregulation of HNRNPA1 inhibited tumor growth and increased CD8+ T cell infiltration in vivo. Conclusion: Our study demonstrated that targeting HNRNPA1 could produce immunogenic neoantigens that elicit anti-tumor immunity by inducing abnormal mRNA splicing. It suggests that HNRNPA1 may be a potential target for immunotherapy. [ABSTRACT FROM AUTHOR]

Automatically identifying objects similar to the surroundings is a complex and difficult task in real-world scenarios. In addition to the high intrinsic similarity between camouflaged objects and their backgrounds, these objects are usually diverse in scale and blurred in appearance. And the deceptive nature of the camouflaged objects introduces lots of noise into the features and generates inaccurate segmentation map extracted by deep learning model. We tackle these problems by proposing a novel dynamic interactive refinement network (DIRNet), which aims to make the features exploit effective details and semantics together as well as discard interference information. Specifically, we utilize bilateral interaction module (BIM) to interact with foreground and background information to conduct contextual exploration, which can capture more meaningful details and refine the confusion. Additionally, in the purpose of retaining the appropriate information and erasing noise, we design an adjacent aggregation interaction module (AAIM) to integrate the adjacent multi-level features with attention coefficients for each layer. The final results are obtained through the dynamic refinement of the BIM and AAIM. Extensive quantitative and qualitative experiments on four public benchmark datasets demonstrate that our proposed DIRNet is an effective COD framework and outperforms 14 state-of-the-art models. [ABSTRACT FROM AUTHOR]

Background: Aberrant DNA replication is the main source of genomic instability that leads to tumorigenesis and progression. MCM2, a core subunit of eukaryotic helicase, plays a vital role in DNA replication. The dysfunction of MCM2 results in the occurrence and progression of multiple cancers through impairing DNA replication and cell proliferation. Conclusions: MCM2 is a vital regulator in DNA replication. The overexpression of MCM2 was detected in multiple types of cancers, and the dysfunction of MCM2 was correlated with the progression and poor prognoses of malignant tumors. According to the altered expression of MCM2 and its correlation with clinicopathological features of cancer patients, MCM2 was thought to be a sensitive biomarker for cancer diagnosis, prognosis, and chemotherapy response. The anti-tumor effect induced by MCM2 inhibition implies the potential of MCM2 to be a novel therapeutic target for cancer treatment. Since DNA replication stress, which may stimulate anti-tumor immunity, frequently occurs in MCM2 deficient cells, it also proposes the possibility that MCM2 targeting improves the effect of tumor immunotherapy. [ABSTRACT FROM AUTHOR]

Mini-chromosome maintenance protein 2 (MCM2) is a potential target for the development of cancer therapeutics. However, small molecule inhibitors targeting MCM2 need further investigation. Molecular dynamics simulation was performed to identify active pockets in the MCM2 protein structure (6EYC). The active pocket was used as a docking model to discover MCM2 inhibitors by using structure-based virtual screening and surface plasmon resonance (SPR) assay. Furthermore, the efficacy of pixantrone targeting MCM2 in ovarian cancer was evaluated in vitro and in vivo. Pixantrone was identified as a novel inhibitor of MCM2 by virtual screening. SPR binding affinity analysis confirmed the direct binding of pixantrone to MCM2 protein. Pixantrone significantly reduced the viability of ovarian cancer cells A2780 and SKOV3 in a dose- and time-dependent manner. In addition, pixantrone inhibited DNA replication, and induced cell cycle arrest and apoptosis in ovarian cancer cells via targeting MCM2. Knockdown of MCM2 could attenuate the inhibitory activity of pixantrone in ovarian cancer cells. Furthermore, pixantrone significantly suppressed ovarian cancer growth in the A2780 cell xenograft mouse model and showed favorable safety. These findings suggest that pixantrone may be a promising drug for ovarian cancer patients by targeting MCM2 in the clinic. [Display omitted] • Pixantrone identified as a novel MCM2 inhibitor through virtual screening. • Surface plasmon resonance confirms direct binding of pixantrone to MCM2 protein. • Pixantrone inhibits ovarian cancer cell proliferation and induces apoptosis. • Knockdown of MCM2 attenuates the inhibitory effect of pixantrone on cancer cells. • Pixantrone significantly suppresses ovarian cancer growth in a mouse model. [ABSTRACT FROM AUTHOR]

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy. Despite the initial resection and chemotherapeutic treatment, relapse is common, which leads to poor survival rates in patients. A primary cause of recurrence is the persistence of ovarian cancer stem cells (OCSCs) with high tumorigenicity and chemoresistance. To achieve a better therapeutic response in EOC relapse, the mechanisms underlying acquired chemoresistance associated with relapse-initiating OCSCs need to be studied. Transcriptomes of both chemosensitive primary and chemoresistant relapse EOC samples were obtained from ICGC OV-AU dataset for differential expression analysis. The upregulated genes were further studied using KEGG and GO analysis. Significantly increased expression of eighteen CSC-related genes was found in chemoresistant relapse EOC groups. Upregulation of the expression in four hub genes including WNT3A, SMAD3, KLF4, and PAX6 was verified in chemoresistant relapse samples via immunohistochemistry staining, which confirmed the existence and enrichment of OCSCs in chemoresistant relapse EOC. KEGG and GO enrichment analysis in microarray expression datasets of isolated OCSCs indicated that quiescent state, increased ability of drug efflux, and enhanced response to DNA damage may have caused the chemoresistance in relapse EOC patients. These findings demonstrated a correlation between OCSCs and acquired chemoresistance and illustrated potential underlying mechanisms of OCSC-initiated relapse in EOC patients. Meanwhile, the differentially expressed genes in OCSCs may serve as novel preventive or therapeutic targets against EOC recurrence in the future. [ABSTRACT FROM AUTHOR]

In the age of information explosion, image classification is the key technology of dealing with and organizing a large number of image data. Currently, the classical image classification algorithms are mostly based on RGB images or grayscale images, and fail to make good use of the depth information about objects or scenes. The depth information in the images has a strong complementary effect, which can enhance the classification accuracy significantly. In this paper, we propose an image classification technology using principal component analysis based on multi-view depth characters. In detail, firstly, the depth image of the original image is estimated; secondly, depth characters are extracted from the RGB views and the depth view separately, and then the reducing dimension operation through the PCA is implemented. Eventually, the SVM is applied to image classification. The experimental results show that the method has good performance. [ABSTRACT FROM AUTHOR]

Background: Ovarian cancer (OC) is one of the most malignant tumors in the female reproductive system, with a poor prognosis. Various responses to treatments including chemotherapy and immunotherapy are observed among patients due to their individual characteristics. Applicable prognostic markers could make it easier to refine risk stratification for OC patients. Autophagy is closely implicated in the occurrence and development of tumors, including OC. Whether autophagy -related genes can be used as prognostic markers for OC patients remains unclear. Methods: The gene transcriptome data of 374 OC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The correlation between the autophagy levels and outcomes of OC patients was identified through the single sample gene set enrichment analysis (ssGSEA). Recognized molecular markers of autophagy in different clinical specimens were detected by immunohistochemistry (IHC) assay. The gene set enrichment analysis (GSEA), ESTIMATE, and CIBERSORT analysis were applied to explore the correlation of autophagy with the tumor immune microenvironment (TIME). Single-cell RNA-sequencing (scRNA-seq) data from seven OC patients were included for characterizing cell-cell interaction patterns of autophagy-high or low tumor cells. Machine learning, Stepwise Cox regression and LASSO-Cox analysis were used to screen autophagy hub genes, which were used to establish an autophagy-related signature for prognosis evaluation. Four tumor immunotherapy cohorts were obtained from the GEO (Gene Expression Omnibus) database and the literature for autophagy risk score validation. Results: The autophagy levels were closely related to the prognosis of the OC patients. Additionally, the autophagy levels were correlated with TIME status including immune score, and immune-cell infiltration. The scRNA-seq analysis found that tumor cells with high or low autophagy levels had different interactions with immune cells, especially macrophages. Eight autophagy-hub genes (ZFYVE1, AMBRA1, LAMP2, TRAF6, PDPK1, ATG2B, DAPK1 and TP53INP2) were screened for an autophagy-related signature. According to this signature, higher risk score was correlated with poor prognosis and better immunotherapy response in the OC patients. Conclusions: The autophagy-related signature is applicable to predict the prognosis and immune checkpoint inhibitors (ICIs) therapy efficiency in OC patients. It is possible to identify OC patients who will respond to ICIs therapy and have a favorable prognosis, although more verification is needed. [ABSTRACT FROM AUTHOR]

Aerial-view geo-localization tends to determine an unknown position through matching the drone-view image with the geo-tagged satellite-view image. This task is mostly regarded as an image retrieval problem. The key underpinning this task is to design a series of deep neural networks to learn discriminative image descriptors. However, existing methods meet large performance drops under realistic weather, such as rain and fog, since they do not take the domain shift between the training data and multiple test environments into consideration. To minor this domain gap, we propose a Multiple-environment Self-adaptive Network (MuSe-Net) to dynamically adjust the domain shift caused by environmental changing. In particular, MuSe-Net employs a two-branch neural network containing one multiple-environment style extraction network and one self-adaptive feature extraction network. As the name implies, the multiple-environment style extraction network is to extract the environment-related style information, while the self-adaptive feature extraction network utilizes an adaptive modulation module to dynamically minimize the environment-related style gap. Extensive experiments on three widely-used benchmarks, i.e. , University-1652, SUES-200, and CVUSA, demonstrate that the proposed MuSe-Net achieves a competitive result for geo-localization in multiple environments. Furthermore, we observe that the proposed method also shows great potential to the unseen extreme weather, such as mixing the fog, rain and snow. • Identifying one key challenge in visual geo-localization: weather and illumination changes. • Presenting MuSe-Net to alleviate the interference caused by environmental changes. • Designing Residual SPADE for efficient training and feature discrimination boosting. • Results on three geo-localization benchmarks confirm the superiority of our MuSe-Net. [ABSTRACT FROM AUTHOR]

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of noise interference in road defect detection, caused by various environmental factors or acquisition equipment. In this article, we add three different levels of salt & pepper noise to the road defect dataset and propose a novel anti-noise dual-branch network (ADNet). The proposed ADNet leverages two backbone networks equipped with the dual-branch interaction (DI) modules to learn the defect information from noise and clear images for improving noise immunity. Then, the weighted feature representation (WFR) module is designed to extract more context-aware cues from the multi-level feature. Additionally, the region perception unit is proposed, where channel-spatial attention optimization (CSAO) module extracts more defect region information by utilizing the attention mechanism and multi-scale refinement (MR) optimizes the boundary information with the U-Net structure. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art methods, making it a promising solution for detecting road defects in noisy environments. [ABSTRACT FROM AUTHOR]

Optical coherence tomography angiography (OCTA) offers critical insights into the retinal vascular system, yet its full potential is hindered by challenges in precise image segmentation. Current methodologies struggle with imaging artifacts and clarity issues, particularly under low-light conditions and when using various high-speed CMOS sensors. These challenges are particularly pronounced when diagnosing and classifying diseases such as branch vein occlusion (BVO). To address these issues, we have developed a novel network based on topological structure generation, which transitions from superficial to deep retinal layers to enhance OCTA segmentation accuracy. Our approach not only demonstrates improved performance through qualitative visual comparisons and quantitative metric analyses but also effectively mitigates artifacts caused by low-light OCTA, resulting in reduced noise and enhanced clarity of the images. Furthermore, our system introduces a structured methodology for classifying BVO diseases, bridging a critical gap in this field. The primary aim of these advancements is to elevate the quality of OCTA images and bolster the reliability of their segmentation. Initial evaluations suggest that our method holds promise for establishing robust, fine-grained standards in OCTA vascular segmentation and analysis. [ABSTRACT FROM AUTHOR]

RGB-T salient object detection (SOD) has made significant progress in recent years. However, most existing works are based on heavy models, which are not applicable to mobile devices. Additionally, there is still room for improvement in the design of cross-modal feature fusion and cross-level feature fusion. To address these issues, we propose a lightweight cross-modal information mutual reinforcement network for RGB-T SOD. Our network consists of a lightweight encoder, the cross-modal information mutual reinforcement (CMIMR) module, and the semantic-information-guided fusion (SIGF) module. To reduce the computational cost and the number of parameters, we employ the lightweight module in both the encoder and decoder. Furthermore, to fuse the complementary information between two-modal features, we design the CMIMR module to enhance the two-modal features. This module effectively refines the two-modal features by absorbing previous-level semantic information and inter-modal complementary information. In addition, to fuse the cross-level feature and detect multiscale salient objects, we design the SIGF module, which effectively suppresses the background noisy information in low-level features and extracts multiscale information. We conduct extensive experiments on three RGB-T datasets, and our method achieves competitive performance compared to the other 15 state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Bokeh effect is usually used to highlight major contents in an image. Limited by the small sensors, cameras on smartphones are less sensitive to the depth information and cannot directly produce bokeh effect as pleasant as digital single lens reflex cameras. To address this problem, a depth-guided deep filtering network, called DDFN, is proposed in this study. Specifically, the focused region detection block is designed to detect the salient areas, and the depth estimated block is introduced to estimate depth maps from full-focus images. Further, combining depth maps and focused features, an adaptive rendering block is proposed to synthesize bokeh effect with adaptive cross 1-D filters. Both quantitative and qualitative evaluations on the public datasets demonstrate that the proposed model performs favorably against state-of-the-art methods in terms of rendering effects and has lower computational cost, e.g., 24.07 dB PSNR on EBB! dataset and 0.45 s inference times for a 512 × 768 image on a Snapdragon 865 mobile processor. [ABSTRACT FROM AUTHOR]

This paper introduces a novel video saliency model for salient object detection in videos. Firstly, we generate multi-level deep features via a symmetrical convolutional neural network, in which the inputs are the current frame and the optical flow image. Then, the multi-level deep features are integrated in a hierarchical manner using a fusion network, which deploys attention module to make a selection for deep features. Lastly, the integrated deep feature is combined with the boundary information originated from shallow layer of the feature extraction networks, and the saliency map is generated by the saliency prediction step. The key advantages of our model lie on the attention module, the hierarchical integration and the boundary information, in which the former one acts as weight filter and is used to select the most salient regions in deep features, the middle one gives an effective integration manner for features from different layers and the last one provides well-defined boundaries for saliency map. Extensive experiments are performed on two challenging video dataset, and the experimental results show that our model consistently outperforms the state-of-the-art saliency models in a large margin. [ABSTRACT FROM AUTHOR]

To boost the product quality, numerous saliency-based surface defect detection methods have been devoted to the areas of industrial production, construction consumable, road construction. However, the existing salient object detection (SOD) methods not only consume a significant amount of computing resources but also fail to meet the detection efficiency requirements of enterprises. Therefore, this paper proposes a lightweight semantics-aware multi-level feature interaction network (SMINet), to address the above issues. In the encoder phase, we integrate multiple adjacent level features in the cross-layer feature fusion (CFF) module to alleviate the discrepancy between multi-scale features. In the decoder phase, we first employ the semantic-aware feature extraction (SFE) module to mine the location cues embedded in the high-level features. Afterwards, we introduce the detail-aware context attention (DCA) module based on the attention mechanism to recover more spatial details. Extensive experiments on four surface defect datasets validate that our SMINet outperforms the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Motivation Light-field microscopy (LFM) is a compact solution to high-speed 3D fluorescence imaging. Usually, we need to do 3D deconvolution to the captured raw data. Although there are deep neural network methods that can accelerate the reconstruction process, the model is not universally applicable for all system parameters. Here, we develop AutoDeconJ, a GPU-accelerated ImageJ plugin for 4.4× faster and more accurate deconvolution of LFM data. We further propose an image quality metric for the deconvolution process, aiding in automatically determining the optimal number of iterations with higher reconstruction accuracy and fewer artifacts. Results Our proposed method outperforms state-of-the-art light-field deconvolution methods in reconstruction time and optimal iteration numbers prediction capability. It shows better universality of different light-field point spread function (PSF) parameters than the deep learning method. The fast, accurate and general reconstruction performance for different PSF parameters suggests its potential for mass 3D reconstruction of LFM data. Availability and implementation The codes, the documentation and example data are available on an open source at: https://github.com/Onetism/AutoDeconJ.git. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Image demoireing is a multi-faceted image restoration task involving both moire pattern removal and color restoration. In this paper, we raise a general degradation model to describe an image contaminated by moire patterns, and propose a novel multi-scale bandpass convolutional neural network (MBCNN) for single image demoireing. For moire pattern removal, we propose a multi-block-size learnable bandpass filters (M-LBFs), based on a block-wise frequency domain transform, to learn the frequency domain priors of moire patterns. We also introduce a new loss function named Dilated Advanced Sobel loss (D-ASL) to better sense the frequency information. For color restoration, we propose a two-step tone mapping strategy, which first applies a global tone mapping to correct for a global color shift, and then performs local fine tuning of the color per pixel. To determine the most appropriate frequency domain transform, we investigate several transforms including DCT, DFT, DWT, learnable non-linear transform and learnable orthogonal transform. We finally adopt the DCT. Our basic model won the AIM2019 demoireing challenge. Experimental results on three public datasets show that our method outperforms state-of-the-art methods by a large margin. [ABSTRACT FROM AUTHOR]

Simple Summary: Due to most traditional high-throughput experiments are tedious and laborious in identifying potential protein–protein interaction. To better improve accuracy prediction in protein–protein interactions. We proposed a novel computational method that can identify unknown protein–protein interaction efficiently and hope this method can provide a helpful idea and tool for proteomics research. Protein–protein interactions (PPIs) play an essential role in many biological cellular functions. However, it is still tedious and time-consuming to identify protein–protein interactions through traditional experimental methods. For this reason, it is imperative and necessary to develop a computational method for predicting PPIs efficiently. This paper explores a novel computational method for detecting PPIs from protein sequence, the approach which mainly adopts the feature extraction method: Locality Preserving Projections (LPP) and classifier: Rotation Forest (RF). Specifically, we first employ the Position Specific Scoring Matrix (PSSM), which can remain evolutionary information of biological for representing protein sequence efficiently. Then, the LPP descriptor is applied to extract feature vectors from PSSM. The feature vectors are fed into the RF to obtain the final results. The proposed method is applied to two datasets: Yeast and H. pylori, and obtained an average accuracy of 92.81% and 92.56%, respectively. We also compare it with K nearest neighbors (KNN) and support vector machine (SVM) to better evaluate the performance of the proposed method. In summary, all experimental results indicate that the proposed approach is stable and robust for predicting PPIs and promising to be a useful tool for proteomics research. [ABSTRACT FROM AUTHOR]

Cross-view geo-localization is to spot images of the same geographic target from different platforms, e.g., drone-view cameras and satellites. It is challenging in the large visual appearance changes caused by extreme viewpoint variations. Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas. In this work, we argue that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization. Specifically, we introduce a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner. Without using extra part estimators, LPN adopts a square-ring feature partition strategy, which provides the attention according to the distance to the image center. It eases the part matching and enables the part-wise representation learning. Owing to the square-ring partition design, the proposed LPN has good scalability to rotation variations and achieves competitive results on three prevailing benchmarks, i.e., University-1652, CVUSA and CVACT. Besides, we also show the proposed LPN can be easily embedded into other frameworks to further boost performance. [ABSTRACT FROM AUTHOR]

Background: Accumulating evidence has revealed that aberrant microRNA (miRNA) expression can affect the development of chemotherapy drug resistance by modulating the expression of relevant target proteins. Emerging evidence has demonstrated that miR-133a participates in the tumorigenesis of various cancers. However, whether miR-133a is associated with cisplatin resistance in ovarian cancer remains unclear. Objective: To investigate the role of miR-133a in the development of cisplatin resistance in ovarian cancer. Methods: MiR-133a expression in cisplatin-resistant ovarian cancer cell lines was assessed by reverse-transcription quantitative PCR (RT–qPCR). A cell counting kit-8 (CCK-8) assay was used to evaluate the viability of tumour cells treated with cisplatin in the presence or absence of miR-133a. A luciferase reporter assay was used to analyse the binding of miR-133a with the 3′ untranslated region (3′UTR) of YES proto-oncogene 1 (YES1). The YES1 expression level was analysed using a dataset from the International Cancer Genome Consortium (ICGC) and assessed by RT–qPCR and western blotting in vitro. The roles and mechanisms of YES1 in cell functions were further probed via gain- and loss-of-function analysis. Results: The expression of miR-133a was significantly decreased in cisplatin-resistant ovarian cancer cell lines (A2780-DDP and SKOV3-DDP), and the overexpression of the miR-133a mimic reduced cisplatin resistance in A2780-DDP and SKOV3-DDP cells. Treatment with the miR-133a inhibitor increased cisplatin sensitivity in normal A2780 and SKOV3 cells. MiR-133a binds the 3'UTR of YES1 and downregulates its expression. Bioinformatics analysis revealed that YES1 expression was upregulated in recurrent cisplatin-resistant ovarian cancer tissue, and in vitro experiments also verified its upregulation in cisplatin-resistant cell lines. Furthermore, we discovered that miR-133a downregulated the expression of YES1 and thus inhibited cell autophagy to reduce cisplatin resistance. Yes1 knockdown significantly suppressed the cisplatin resistance of ovarian cancer cells by inhibiting autophagy in vitro. Xenograft tumour implantation further demonstrated that Yes1 overexpression promoted ovarian tumour development and cisplatin resistance. Conclusions: Our results suggest that the miR-133a/YES1 axis plays a critical role in cisplatin resistance in human ovarian cancer by regulating cell autophagy, which might serve as a promising therapeutic target for ovarian cancer chemotherapy treatment in the future. [ABSTRACT FROM AUTHOR]

Cross modal retrieval can retrieve images through a text query and vice versa. In recent years, cross modal retrieval has attracted extensive attention. The purpose of most now available cross modal retrieval methods is to find a common subspace and maximize the different modal correlation. To generate specific representations consistent with cross modal tasks, this paper proposes a novel cross modal retrieval framework, which integrates feature learning and latent space embedding. In detail, we proposed a deep CNN and a shallow CNN to extract the feature of the samples. The deep CNN is used to extract the representation of images, and the shallow CNN uses a multi‐dimensional kernel to extract multi‐level semantic representation of text. Meanwhile, we enhance the semantic manifold by constructing cross modal ranking and within‐modal discriminant loss to improve the division of semantic representation. Moreover, the most representative samples are selected by using online sampling strategy, so that the approach can be implemented on a large‐scale data. This approach not only increases the discriminative ability among different categories, but also maximizes the relativity between different modalities. Experiments on three real word datasets show that the proposed method is superior to the popular methods. [ABSTRACT FROM AUTHOR]

Re-ranking is becoming a critical part of retrieved based Person re-identification algorithms. Existing re-ranking methods always require lots of queries and memory to go through the k -nearest neighbors. To solve this problem, we introduce a Feature Relation Map (FRM) to mine the latent relation between the k -neighbors through convolution neural network, and propose a metric learning based Similarity Evaluation (SE) model to obtain the re-ranking distance from the FRM. The dilated convolution is then introduced by concatenating the dilated convolution kernels and normal convolution kernels along the channel dimension in the improvement of the SE model (named SE-d model), to allow the SE model to efficiently compare more samples pairs for obtaining the final re-ranking distance. Further, we embedding out FRM-SE model to the existing re-ranking methods to prove the effectiveness of our re-ranking model. The experiments on Market1501, CUHK03, DukeMTMC and MSMT17 benchmarks illustrates the superiority of proposed method to the state-of-the-art re-ranking methods. Although the SE model make a great improve performance, the SE-d model gains an increase of 0.31%, 0.63% in top-1 respectably compared with the SE model in the DukeMTMC and MSMT17 datasets. Meanwhile, the SE-d model speed up the model convergence and shorten the training time. Furthermore, in the transfer learning setting, the model trained on either Market1501, CUHK03 or DukeMTMC can achieve a comparable accuracy improvement on the MSMT17 dataset, which validates the generalization of our SE model. [ABSTRACT FROM AUTHOR]

Salient object detection methods based on two-modal images have achieved remarkable success with the aid of image acquisition equipment. However, environmental factors often interfere with the Depth and Thermal maps, rendering them ineffective in providing object information. To address this weakness, we utilize the VDT dataset, which includes Visible, Depth, and Thermal images, and propose a triple-modal interaction encoder and multi-scale fusion decoder network (TMNet) to highlight the salient regions. The triple-modal interaction encoder comprises the separation context-aware feature module, channel-wise fusion module, and triple-modal refinement and fusion module, enabling us to fully explore and utilize the complementarity between Visible, Depth, and Thermal information. The multi-scale fusion decoder involves the semantic-aware localizing module and contour-aware refinement module to extract and fuse the location and boundary information, yielding a high-quality saliency map. Extensive experiments on the public VDT-2048 dataset demonstrate that our TMNet outperforms existing state-of-the-art methods in terms of all evaluation metrics. • This paper proposes a triple-modal interaction encoder and multi-scale fusion decoder network (TMNet) to highlight the salient regions. • The triple-modal interaction encoder comprises the separation context-aware feature module, channel-wise fusion module, and triple-modal refinement and fusion module to explore and utilize the complementarity between Visible, Depth, and Thermal information. • The multi-scale fusion decoder involves the semantic-aware localizing module and contour-aware refinement module to extract and fuse the location and boundary information, yielding a high-quality saliency map. • Extensive experiments on the public VDT-2048 dataset demonstrate that our TMNet outperforms existing state-of-the-art methods in terms of all evaluation metrics. [ABSTRACT FROM AUTHOR]

Surface defect detection has become more and more important in the industrial manufacture and engineering application in recent years. However, due to the lack of overall perception and interaction among features layers, lots of computer vision-based detection methods cannot grab the complete details of defects when dealing with complex scenes, such as low contrast and irregular shape. Therefore, in this paper, we propose a Context-aware Aggregation Network (CANet) to accurately pop-out the defects, where we focus on the mining of context cues and the fusion of multiple context features. To be specific, embarking on the multi-level deep features extracted by encoder, we first deploy a sufficient exploration to dig the context information by deploying the weighted convolution pyramid (WCP) module, which extracts multi-scale context features, transfers the information flow between different resolution features, and fuses the features with same resolution. By this way, we can obtain the effective context pyramid features. Then, the decoder deploys the weighted context attention (WCA) module to filter the irrelevant information from context features and employs the cascaded fusion structure (CFS) to aggregate the multiple context cues in a hierarchical way. Following this way, the generated high-quality saliency maps can highlight the defects accurately and completely. Extensive experiments are performed on four public datasets, and the results firmly prove the effectiveness and superiority of the proposed CANet under different evaluation metrics. [ABSTRACT FROM AUTHOR]

With the development of productivity, people set higher demands on the quality of steel. In recent years, artificial intelligence, especially deep learning-based computer vision technology has attracted great attention, which can be used for detecting steel surface defects such as scratches, patches, and rust spots. However, due to the complexity of the strip steel surface, it is still a challenge to accurately and effectively detect defect regions by the existing defects detection methods. Therefore, we propose a unique saliency model, i.e., deeper feature integration network to highlight the defect regions on the strip steel surface. To be specific, after each encoder stage, we introduce the multiscale global feature extraction module to elevate the multiscale deep features from the encoder. Meanwhile, we deploy the deeper feature extraction module, which contains a bidirectional feature extraction unit, to dig the effective representation for defects. Particularly, the forward one is equipped with a channel-space weighted module and the backward one is equipped with a split attention module. After that, the features from the two branches are progressively integrated by the decoder, yielding the final high-quality saliency maps that give a good depiction of the strip steel surface defects. Extensive experiments are executed on the public dataset, and the comparison results show that our model performs better than the 15 state-of-the-art methods, which proves the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

The dense connection is a powerful technique to build wider and deeper convolution neural networks (CNNs) for handling several computer vision tasks. Despite the excellent performance, it consumes numerous parameters and produces a large weight model file. We studied the distribution of convolution layers and proposed a hard parameter sharing approach known as convolution pool (CP) for compressing dense-connection-based image restoration CNN models. CP is used to reallocate the parameters to specific convolution layers to ensure that some can be shared in different layers. We design a set of dense-connection-based baselines for three typical image restoration tasks, including image denoising, super-resolution, and JPEG deblocking, to validate the performance of the proposed method. Moreover, we comprehensively analyze the potential problems by introducing CP, including group convolution, dilated convolution, and modeling efficiency. Experimental results demonstrate that the proposed method can efficiently achieve an impressive compression rate with negligible performance reduction. [ABSTRACT FROM AUTHOR]

The rapidly growing location-based social network (LBSN) has become a promising platform for studying users' mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial and temporal influences on location recommendation; however, most existing approaches build a universal spatial–temporal model for all users despite the fact that users always demonstrate heterogeneous check-in behavior patterns. In order to realize truly personalized location recommendations, we propose a Gaussian process based model for each user to systematically and non-linearly combine temporal and spatial information to predict the user's displacement from their currently checked-in location to the next one. The locations whose distances to the user's current checked-in location are the closest to the predicted displacement are recommended. We also propose an enhancement to take into account category information of locations for semantic-aware recommendation. A unified recommendation framework called spatial–temporal–semantic (STS) is introduced to combine displacement prediction and the semantic-aware enhancement to provide final top-N recommendation. Extensive experiments over real datasets show that the proposed STS framework significantly outperforms the state-of-the-art location recommendation models in terms of precision and mean reciprocal rank (MRR). [ABSTRACT FROM AUTHOR]