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Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The 'CMRxRecon' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community., (© 2024. The Author(s).)

Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) ( P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) ( P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) ( P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Magnesium ions (Mg 2+ ) are essential for the folding, functional expression, and structural stability of RNA molecules. However, predicting Mg 2+ -binding sites in RNA molecules based solely on RNA structures is still challenging. The molecular surface, characterized by a continuous shape with geometric and chemical properties, is important for RNA modelling and carries essential information for understanding the interactions between RNAs and Mg 2+ ions. Here, we propose an approach named RNA-magnesium ion surface interaction fingerprinting (RMSIF), a geometric deep learning-based conceptual framework to predict magnesium ion binding sites in RNA structures. To evaluate the performance of RMSIF, we systematically enumerated decoy Mg 2+ ions across a full-space grid within the range of 2 to 10 Å from the RNA molecule and made predictions accordingly. Visualization techniques were used to validate the prediction results and calculate success rates. Comparative assessments against state-of-the-art methods like MetalionRNA, MgNet, and Metal3DRNA revealed that RMSIF achieved superior success rates and accuracy in predicting Mg 2+ -binding sites. Additionally, in terms of the spatial distribution of Mg 2+ ions within the RNA structures, a majority were situated in the deep grooves, while a minority occupied the shallow grooves. Collectively, the conceptual framework developed in this study holds promise for advancing insights into drug design, RNA co-transcriptional folding, and structure prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

BACKGROUND. The confounder-corrected chemical shift-encoded MRI (CSE-MRI) sequence used to determine proton density fat fraction (PDFF) for hepatic fat quantification is not widely available. As an alternative, hepatic fat can be assessed by a two-point Dixon method to calculate signal fat fraction (FF) from conventional T1-weighted in- and opposed-phase (IOP) images, although signal FF is prone to biases, leading to inaccurate quantification. OBJECTIVE. The purpose of this study was to compare hepatic fat quantification by use of PDFF inferred from conventional T1-weighted IOP images and deep-learning convolutional neural networks (CNNs) with quantification by use of two-point Dixon signal FF with CSE-MRI PDFF as the reference standard. METHODS. This study entailed retrospective analysis of data from 292 participants (203 women, 89 men; mean age, 53.7 ± 12.0 [SD] years) enrolled at two sites from September 1, 2017, to December 18, 2019, in the Strong Heart Family Study (a prospective population-based study of American Indian communities). Participants underwent liver MRI (site A, 3 T; site B, 1.5 T) including T1-weighted IOP MRI and CSE-MRI (used to reconstruct CSE PDFF and CSE R2* maps). With CSE PDFF as reference, a CNN was trained in a random sample of 218 (75%) participants to infer voxel-by-voxel PDFF maps from T1-weighted IOP images; testing was performed in the other 74 (25%) participants. Parametric values from the entire liver were automatically extracted. Per-participant median CNN-inferred PDFF and median two-point Dixon signal FF were compared with reference median CSE-MRI PDFF by means of linear regression analysis, intraclass correlation coefficient (ICC), and Bland-Altman analysis. The code is publicly available at github.com/kang927/CNN-inference-of-PDFF-from-T1w-IOP-MR. RESULTS. In the 74 test-set participants, reference CSE PDFF ranged from 1% to 32% (mean, 11.3% ± 8.3% [SD]); reference CSE R2* ranged from 31 to 457 seconds -1 (mean, 62.4 ± 67.3 seconds -1 [SD]). Agreement metrics with reference to CSE PDFF for CNN-inferred PDFF were ICC = 0.99, bias = -0.19%, 95% limits of agreement (LoA) = (-2.80%, 2.71%) and for two-point Dixon signal FF were ICC = 0.93, bias = -1.11%, LoA = (-7.54%, 5.33%). CONCLUSION. Agreement with reference CSE PDFF was better for CNN-inferred PDFF from conventional T1-weighted IOP images than for two-point Dixon signal FF. Further investigation is needed in individuals with moderate-to-severe iron overload. CLINICAL IMPACT. Measurement of CNN-inferred PDFF from widely available T1-weighted IOP images may facilitate adoption of hepatic PDFF as a quantitative bio-marker for liver fat assessment, expanding opportunities to screen for hepatic steatosis and nonalcoholic fatty liver disease.

Introduction: Microvascular invasion (MVI) is a known risk factor for prognosis after R0 liver resection for hepatocellular carcinoma (HCC). The aim of this study was to develop a deep learning prognostic prediction model by incorporating a new factor of MVI area to the other independent risk factors., Methods: Consecutive patients with HCC who underwent R0 liver resection from January to December 2016 at the Eastern Hepatobiliary Surgery Hospital were included in this retrospective study. For patients with MVI detected on resected specimens, they were divided into two groups according to the size of the maximal MVI area: the small-MVI group and the large-MVI group., Results: Of 193 patients who had MVI in the 337 HCC patients, 130 patients formed the training cohort and 63 patients formed the validation cohort. The large-MVI group of patients had worse overall survival (OS) when compared with the small-MVI group (p = 0.009). A deep learning model was developed based on the following independent risk factors found in this study: MVI stage, maximal MVI area, presence/absence of cirrhosis, and maximal tumor diameter. The areas under the receiver operating characteristic of the deep learning model for the 1-, 3-, and 5-year predictions of OS were 80.65, 74.04, and 79.44, respectively, which outperformed the traditional COX proportional hazards model., Conclusion: The deep learning model, by incorporating the maximal MVI area as an additional prognostic factor to the other previously known independent risk factors, predicted more accurately postoperative long-term OS for HCC patients with MVI after R0 liver resection., (© 2022. Asian Pacific Association for the Study of the Liver.)

Background: Assessment of microvascular invasion (MVI) in intrahepatic cholangiocarcinoma (ICC) by using a noninvasive method is an unresolved issue. Deep learning (DL) methods based on multiparametric fusion of MR images have the potential of preoperative assessment of MVI., Purpose: To investigate whether a multiparametric fusion DL model based on MR images can be used for preoperative assessment of MVI in ICC., Study Type: Retrospective., Population: A total of 519 patients (200 females and 319 males) with a single ICC were categorized as a training (n = 361), validation (n = 90), and an external test cohort (n = 68)., Field Strength/sequence: A 1.5 T and 3.0 T; axial T2-weighted turbo spin-echo sequence, diffusion-weighted imaging with a single-shot spin-echo planar sequence, and dynamic contrast-enhanced (DCE) imaging with T1-weighted three-dimensional quick spoiled gradient echo sequence., Assessment: DL models of multiparametric fusion convolutional neural network (CNN) and late fusion CNN were both constructed for evaluating MVI in ICC. Gradient-weighted class activation mapping was used for visual interpretation of MVI status in ICC., Statistical Tests: The DL model performance was assessed through the receiver operating characteristic curve (ROC) analysis, and the area under the ROC curve (AUC) with the accuracy, sensitivity, and specificity were measured. P value < 0.05 was considered as statistical significance., Results: In the external test cohort, the proposed multiparametric fusion DL model achieved an AUC of 0.888 with an accuracy of 86.8%, sensitivity of 85.7%, and specificity of 87.0% for evaluating MVI in ICC, and the positive predictive value and negative predictive value were 63.2% and 95.9%, respectively. The late fusion DL model achieved a lower AUC of 0.866, with an accuracy of 83.8%, sensitivity of 78.6%, specificity of 85.2% for evaluating MVI in ICC., Data Conclusion: Our DL model based on multiparametric fusion of MRI achieved a good diagnostic performance in the evaluation of MVI in ICC., Level of Evidence: 3 TECHNICAL EFFICACY: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lodging resistance. However, the traditional method of measuring rice culms is destructive, time consuming, and labor intensive. In this study, we used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits. When manual and automatic measurements were compared at the mature stage, the mean absolute percentage errors for major_axis_culm, minor_axis_culm, and wall_thickness_culm in 104 indica rice accessions were 6.03%, 5.60%, and 9.85%, respectively, and the R 2 values were 0.722 and 0.544, respectively. The modeling results indicated that this method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in ∼4.6 min per plant; this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance.R 2 values were 0.722 and 0.544, respectively. The modeling results indicated that this method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in ∼4.6 min per plant; this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance., Competing Interests: The authors declare that they have no competing interests., (© 2021 The Author(s).)

PurposeDelayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TINP ) to suppress the background myocardial signal is required. The purpose of this study was to assess the feasibility of automated selection of TINP using a convolutional neural network (CNN). We hypothesized that a CNN may use spatial and temporal imaging characteristics from an inversion-recovery scout to select TINP , without the aid of a human observer.MethodsWe retrospectively collected 425 clinically acquired cardiac MRI exams performed at 1.5 T that included inversion-recovery scout acquisitions. We developed a VGG19 classifier ensembled with long short-term memory to identify the TINP . We compared the performance of the ensemble CNN in predicting TINP against ground truth, using linear regression analysis. Ground truth was defined as the expert physician annotation of the optimal TI. In a backtrack approach, saliency maps were generated to interpret the classification outcome and to increase the model's transparency.ResultsPrediction of TINP from our ensemble VGG19 long short-term memory closely matched with expert annotation (ρ = 0.88). Ninety-four percent of the predicted TINP were within ±36 ms, and 83% were at or after expert TI selection.ConclusionIn this study, we show that a CNN is capable of automated prediction of myocardial TI from an inversion-recovery experiment. Merging the spatial and temporal characteristics of the VGG-19 and long short-term-memory CNN structures appears to be sufficient to predict myocardial TI from TI scout.

Microvascular invasion of hepatocellular carcinoma is an important factor affecting tumor recurrence after liver resection and liver transplantation. There are many ways to classify microvascular invasion, however, an international consensus is urgently needed. Recently, artificial intelligence has emerged as an important tool for improving the clinical management of hepatocellular carcinoma. Many studies about microvascular invasion currently focus on preoperative and prognosis prediction of microvascular invasion using artificial intelligence. In this paper, we review the definition and staging of microvascular invasion, especially the diagnosis of it by using artificial intelligence. In preoperative prediction, deep learning based on multimodal data modeling of radiomics-screened features, clinical features, and medical images is currently the most effective means. In prognostic prediction, pathology is the gold standard, and the techniques used should more effectively utilize the global features of the pathology images. [ABSTRACT FROM AUTHOR]

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was −2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model. [ABSTRACT FROM AUTHOR]

Simple Summary: The timely and accurate detection of ewe estrus behavior in precision animal husbandry is an important research topic. The timely detection of estrus ewes in mutton sheep breeding can not only protect the welfare of ewes themselves, but also improve the interests of breeding enterprises. With the continuous increase in the scale of mutton sheep breeding and the gradual intensification of breeding methods, the traditional manual detection methods require high labor intensity, and the contact sensor detection methods will cause stress reaction problems in ewes. In recent years, the rapid development of deep learning technology has brought on new possibilities. We propose a method for the recognition ewe estrus based on a multi-target detection layer neural network. The results show that the method can meet the requirements of timely and accurate detection of ewe estrus behavior in large-scale mutton sheep breeding. There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model's ability to learn shallow information and improving the model's ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming. [ABSTRACT FROM AUTHOR]

Simple Summary: Identifying bird species is crucial in various bird monitoring tasks. In this study, we implemented a bird species recognition method based on visual features by convolutional neural networks. This paper proposed an attention-guided data enhancement method and a decoupled knowledge distillation model compression method. Using the two methods, we created a novel and efficient lightweight fine-grained bird classification model. The model not only achieved high accuracy in bird classification but was also user-friendly for edge devices such as mobile. Our work may be useful for bird species recognition and bird biodiversity monitoring. Classifying birds accurately is essential for ecological monitoring. In recent years, bird image classification has become an emerging method for bird recognition. However, the bird image classification task needs to face the challenges of high intraclass variance and low inter-class variance among birds, as well as low model efficiency. In this paper, we propose a fine-grained bird classification method based on attention and decoupled knowledge distillation. First of all, we propose an attention-guided data augmentation method. Specifically, the method obtains images of the object's key part regions through attention. It enables the model to learn and distinguish fine features. At the same time, based on the localization–recognition method, the bird category is predicted using the object image with finer features, which reduces the influence of background noise. In addition, we propose a model compression method of decoupled knowledge distillation. We distill the target and nontarget class knowledge separately to eliminate the influence of the target class prediction results on the transfer of the nontarget class knowledge. This approach achieves efficient model compression. With 67% fewer parameters and only 1.2 G of computation, the model proposed in this paper still has a 87.6% success rate, while improving the model inference speed. [ABSTRACT FROM AUTHOR]

Camouflaged object detection (COD) is an emerging visual detection task, which aims to locate and distinguish the disguised target in complex backgrounds by imitating the human visual detection system. Recently, COD has attracted increasing attention in computer vision, and a few models of camouflaged object detection have been successfully explored. However, most existing works primarily focus on modeling camouflaged object detection over in-depth analyzing existing COD structures. To the best of our knowledge, a systematic review for COD has not been publicly reported, especially for recently proposed deep learning-based COD models. To make up this vacancy, we firstly proposed a comprehensive review on both COD models and public benchmark datasets and provide potential directions for future COD studies. Specifically, we conduct a comprehensive summary of 39 existing COD models from 1998 to 2021. And then, to facilitate subsequent research on COD, we classify the existing structures into two categories, 27 traditional handcrafted feature-based structures and 12 structures based on deep learning. In addition, we further group traditional handcrafted feature-based structures into six sub-classes based on the detection mechanism: texture, color, motion, intensity, optical flow, and multi-modal fusion. Furthermore, we take an in-depth analysis of the deep learning-based structure based on both detection motivation and detection performance and evaluate the performance of each structure. Moreover, we sum up four widely used COD datasets and describe the details of each one. Finally, we also discuss the limitations of COD and the corresponding solutions to improve detection accuracy. We still mention the relevant applications of camouflaged object detection and its future research directions to promote the development of camouflaged object detection. [ABSTRACT FROM AUTHOR]

We propose a lightweight neural network-based method to detect the estrus behavior of ewes. Our suggested method is mainly proposed to solve the problem of not being able to detect ewe estrus behavior in a timely and accurate manner in large-scale meat sheep farms. The three main steps of our proposed methodology include constructing the dataset, improving the network structure, and detecting the ewe estrus behavior based on the lightweight network. First, the dataset was constructed by capturing images from videos with estrus crawling behavior, and the data enhancement was performed to improve the generalization ability of the model at first. Second, the original Darknet-53 was replaced with the EfficientNet-B0 for feature extraction in YOLO V3 neural network to make the model lightweight and the deployment easier, thus shortening the detection time. In order to further obtain a higher accuracy of detecting the ewe estrus behavior, we joined the feature layers to the SENet attention module. Finally, the comparative results demonstrated that the proposed method had higher detection accuracy and FPS, as well as a smaller model size than the YOLO V3. The precision of the proposed scheme was 99.44%, recall was 95.54%, F1 value was 97%, AP was 99.78%, FPS was 48.39 f/s, and Model Size was 40.6 MB. This study thus provides an accurate, efficient, and lightweight detection method for the ewe estrus behavior in large-scale mutton sheep breeding. [ABSTRACT FROM AUTHOR]

Spinning reserve based on solar radiation prediction can ensure the secure operation of large-scale grid-connected photovoltaic systems, but irrational spinning reserve can lead to substantial economic losses and even grid collapse. Therefore, it is imperative to identify characteristic patterns of solar radiation and establish an effective solar radiation prediction model. However, existing hybrid models often struggle to efficiently recognize and leverage input features, compromising the robustness of the model. To address this limitation, this study proposes an integrated skip-convolutional network with residual learning and feature extraction (InSCNet), which enhances the capability to represent information data and thereby improves the stability and accuracy of solar radiation prediction by employing a series of feature extraction and prediction blocks with residual learning. InSCNet includes a sophisticated feature extraction module to effectively address the underutilization of feature information, and it incorporates residual learning, skip-convolution, and recursive networks in the prediction module, reducing the risk of gradient vanishing or gradient explosion while enhancing prediction accuracy. Additionally, an interval estimation method with adaptively chosen distributions is introduced, which extends the prediction interval estimation method. The proposed InSCNet is rigorously evaluated using datasets from three major cities in Pakistan. Experimental results demonstrate that InSCNet outperforms existing solutions in both point and interval predictions. • A feature extraction module for solar radiation prediction is designed. • The prediction module is constructed using residual learning and skip-convolution. • An optimization layer of a multi-objective Kepler optimization algorithm is developed. • An interval estimation method with adaptively chosen distributions is introduced. [ABSTRACT FROM AUTHOR]

Liver cancer is a prevalent cancer worldwide and is also the leading cause of cancer-related deaths. Histopathological image diagnosis is taken as the standard to identify liver cancer. However, manually histopathological examination of liver cancer WSIs (Whole Slide Image) is time consuming. To address this issue, researchers have applied the automatic classification methods to recognize cancerous tissues in histopathological images, which can effectively reduce the examination time and improving the recognition efficiency. In recent years, deep learning based methods have been widely used in histopathological image analysis because of their impressive performance level. But only a little research work based on transfer learning has been done on liver cancer histopathological images because the sample dataset is difficult to collected and the classification results lack of effectiveness and pertinence. One issue with fine-grained classification is that using a single patch for classification results in the loss of edge information, degrading classification performance. To solve the problem, this paper proposes a novel deep learning model based on sequence prediction with attention, which is used to identify and classify liver cancer histopathological images. The proposed method uses the surrounding 8 patches to supplement information to the current patch, resulting in better results than other methods. The experimental results show that this method achieves the best F1-score of 95.23% which illustrates that the proposed method is efficient in liver cancer histopathological image classification. [ABSTRACT FROM AUTHOR]

Deep learning has achieved excellent achievements and has become the mainstream in the field of aerial image classification. While obtaining remarkable success, deep learning-based approaches are notoriously dependent on large amounts of labelled data. Few-shot learning uses existing knowledge to learn from few samples and quickly generalizes to new tasks. In this work, we proposed the few-shot learning with deep economic network and teacher knowledge for aerial image classification. Firstly, we performed simplification twice to reduce large-scale parameters and computational effort in deep networks. In the first simplification, the redundancy in feature inputs' main components is reduced, and the implicit information in redundant components is extracted instead of directly discarding the redundant components. The channel and spatial redundancies in deeper layers' inputs are reduced in the second simplification. Secondly, the teacher knowledge guides the random sampling and uses limited samples to improve classification performance. We conducted extensive experiments on NWPU-RESISC45, RSD46-WHU, and UC Merced datasets. The experimental results reveal that the proposed method has better classification accuracy, fewer network parameters, and less computational effort. Experiments on miniImageNet, FC100, CUB, and cross-domain datasets show that our method also maintains advanced classification accuracy on few-shot image classification benchmarks. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel framework for camouflaged object detection (COD), named D $^{2}$ C-Net, which contains two new modules: Dual-branch features extraction (DFE) and gradually refined cross fusion (GRCF). Specifically, the DFE simulates the two-stage detection process of human visual mechanisms in observing camouflage scenes. For the first stage, a dense concatenation is employed to aggregate multilevel features and expand the receptive field. The first stage feature maps are then utilized to extract two-direction guidance information, which benefits the second stage. The GRCF consists of a self-refine attention unit and a cross-refinement unit, with the aim of combining the peer layer features and DFE features for an improved COD performance. The proposed framework outperforms 13 state-of-the-art deep learning-based methods upon three public datasets in terms of five widely used metrics. Finally, we show evidence for the successful applications of the proposed method in the fields of surface defect detection and medical image segmentation. [ABSTRACT FROM AUTHOR]

Background: Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer (PCPMWF) by meta-learning can help to explore the effects of magnetized water and fertilizer irrigation on crops. Method: In this article, we propose a meta-learning optimization model based on the meta-learner LSTM in the field of regression prediction of PCPMWF. In meta-learning, LSTM is used to replace MAML's gradient descent optimizer for regression tasks, enables the meta-learner to learn the update rules of the LSTM, and apply it to update the parameters of the model. The proposed method is compared with the experimental results of MAML and LSTM to verify the feasibility and correctness. Results: The average absolute percentage error of the meta-learning optimization model of meta-learner LSTM is reduced by 0.37% compared with the MAML model, and by 4.16% compared with the LSTM model. The loss value of the meta-learning optimization model in the iterative process drops the fastest and steadily compared to the MAML model and the LSTM model. In cross-domain experiments, the average accuracy of the meta-learning optimized model can still reach 0.833. Conclusions: In the case of few sample, the proposed model is superior to the traditional LSTM model and the basic MAML model. And in the training of cross-domain datasets, this model performs best. [ABSTRACT FROM AUTHOR]

• The deep learning pneumothorax segmentation method obtained state-of-the-art results. • The SDM regression method provided a solution to the blurred boundary problem. • A new pneumothorax segmentation dataset PTX-498 has been released. Accurate segmentation of the pneumothorax from chest X-ray images is of considerable significance for the diagnosis in the emergency department. Recently, deep learning methods have achieved impressive progress in many medical image segmentation tasks. However, the blurred boundary of pneumothorax in chest X-ray images makes it difficult for these algorithms to perform well in pneumothorax segmentation. In this paper, we aim to solve the problem in current learning-based segmentation methods that they often ignore the information of the target boundary and thus fail to generate the result with the correct shape. The proposed new learning framework, DeepSDM, employs the rich information in Signed Distance Map (SDM). The binary segmentation mask and SDM are learned in parallel via the multi-task strategy. Moreover, a boundary-based weighting scheme is adopted in the SDM regression task, forcing the model to focus more on pneumothorax and its contour. The proposed DeepSDM is validated by extensive experiments on the Kaggle SIIM-ACR Pneumothorax Segmentation dataset and our PTX-498 dataset. The results demonstrate that DeepSDM effectively perceives the boundary of pneumothorax and outperforms other state-of-the-art methods. To provoke the research interest in the community, the PTX-498 dataset, codes, and trained models are publicly available at Zenodo and GitHub. [ABSTRACT FROM AUTHOR]

Accurately forecasting power load is essential for utilities to effectively manage their resources, reduce operational costs, and provide improved customer service. However, the current load forecasting lacks the ability to deeply explore data, thus failing to accurately predict both short-term trends and volatility ranges. To address this issue, we construct a novel combined forecasting system based on rough sets, information granulation, deep learning, and multi-objective optimization. In this study, we follow the reasonable granulation criterion for granular computing, which aims to improve the reasonableness and specificity of granular interval prediction under the determination of granularity level, and innovatively propose a novel multi-objective optimization algorithm that can simultaneously constrain the reasonable granulation criterion and theoretically demonstrate the obtained Pareto-optimal solution. Four simulation experiments were conducted using the Australian dataset to evaluate the performance of our proposed system in predicting trend changes and fluctuation ranges of power load. Our results demonstrate that the developed system effectively predicts the trend changes and fluctuation range of power load. Specifically, our system showed a deterministic prediction performance improvement of 13.39% and a granularity interval prediction performance improvement of 6.67% compared to the baseline model. Moreover, we conducted a series of discussion tests to validate the superiority of our system, which further confirmed the effectiveness of our proposed approach. • A multivariate intelligent granularity combination forecasting system (MGCFS) is constructed. • A novel clustering algorithm based on intelligent optimization is presented for information granulation. • Pareto optimal solution is proved theoretically. • A multi- objective swarm intelligent optimization technique is proposed. [ABSTRACT FROM AUTHOR]

Camouflaged Object Detection (COD) aims to detect objects with high similarity to the background. Unlike general object detection, COD is a more challenging task because the target boundaries are vague and the location is difficult to determine. In this paper, we propose a novel COD framework, which consists of two main components, namely, Neighbor Connection Mode (NCM) and Hierarchical Information Transfer (HIT). NCM aggregates the features from the neighboring layers of the encoder network to enhance the complementation of various level information. Our NCM not only reduces the burden of dense connection that consumes a lot of computing memory and redundant features but also weakens the phenomenon of the long-term transmission of context. We also propose a HIT module to transfer the features of different dilated rates inside each level hierarchically, which expands the receptive field of each branch and enhances the relationship between different features. Our method accurately detects camouflaged objects by considering full level information and a large receptive field. The experiments on three COD datasets show that our model achieves state-of-the-art performance. • A double branch framework focuses on camouflaged object detection (COD). • A Neighbor Connection Mode (NCM) is proposed to aggregate adjacent level features. • Hierarchical Information Transfer (HIT) is presented to enlarge receptive fields. [ABSTRACT FROM AUTHOR]

Wind power prediction is crucial for successfully integrating large-scale wind energy with the grid and achieving a carbon-neutral energy mix. However, previous studies encountered challenges to noise reduction, information extraction, error handling, and uncertainty estimation. To address these limitations, this study proposed a novel wind power forecasting system, systematically enhancing forecasting capability by refining the raw time series using horizontal denoising and vertical granulation methods, utilizing a bidirectional deep learning model for capturing nonlinear features and optimizing its core hyperparameters with a newly developed multi-objective enhanced version of metaheuristic algorithm to achieve Pareto optimal solutions, employing the traditional statistical approach for residual linear feature extraction from error series and effective correction, and integrating quantile regression for interval prediction. The predictive performance was evaluated based on wind power, speed, and direction data from two different countries, the system yielded correlation coefficients of 0.9796, 0.9943, 0.9795, and 0.9780 in spring, summer, autumn and winter for China, and 0.9950 for Turkey, demonstrating its pronounced advantages over comparative models. Overall, the proposed system contributes to the advancement of wind-power prediction theory and holds substantial potential for practical applications. • A novel hybrid wind power forecasting system was proposed. • Solve problems of noise interference and high computing burden by data refining. • A strategy to fully extract nonlinear and linear data features was designed. • An enhanced multi-objective golden jackal algorithm was raised for optimization. • Pareto optimal solutions were theoretically proven to be achieved. [ABSTRACT FROM AUTHOR]

Hazy weather seriously threatens human health, providing accurate and reliable air pollutant concentration forecast will be of great scientific value and benefit to residents' livelihood and natural environment. However, existing forecasting models lack outlier detection, feature mapping and uncertainty forecast, which results in unreliable forecasting results. Thus, in this paper, a novel combined deterministic and probabilistic forecasting system based on meta -heuristic algorithm is proposed, which contains multiple data pre-processing, combined deterministic and probabilistic forecast. Firstly, the raw data is pre-processed and analyzed from multiple perspectives to fully extract the chaotic features. Then multi-objective white shark optimizer is utilized to combine the three deep learning models to enhance the forecasting accuracy and stability. Furthermore, the Pin-ball loss function is employed to yield prediction intervals at different confidence levels, and a novel interval pseudo-code is constructed to optimize the prediction intervals thus promoting the probabilistic forecasting reliability. The simulations indicated that the developed system enhances the forecasting accuracy by maximum 60.6772%, 54.9793% and 38.6876% in the three steps of PM 2.5 prediction and up to 64.9001%, 68.4637% in the interval sharpness at significance level 0.05 and 0.1 than the tested models. [ABSTRACT FROM AUTHOR]

• A novel feature processing paradigm and the model based on it are proposed. • The rationality of the method is corroborated in hydrological feature discovery and time-series dependence extraction. • The proposed method shows its excellent performance in term of runoff prediction. • The performance of all models is validated on two proposed constructing noisy datasets. The interaction between hydrological factors is complex and the correlation effects cannot be quantitatively explained from a mechanistic perspective. The extraction of effective features from several hydrological and meteorological data and quantifying their correlation effects to make runoff prediction more accurate and stable is an urgent problem to be solved. In this study, we introduce a structural paradigm for hydrological time series to handle the entire feature space composed of some feature sequences based on deep learning methods, namely, the feature attention mechanism. This method transforms the problem of feature-target association into multiple parallel binary classification problems and assigns attention units to each specific feature in the network. The attention distribution was adjusted by updating the neural network parameters in a supervised manner to generate feature weightings. In addition, two extensions were proposed based on the initial network structure. The three methods were used in a practical study of the upper Yangtze River Basin using three evaluation metrics compared with five benchmark methods. The mean metric values were improved by up to 9.43, 10.05, and 2.65%, respectively. Meanwhile, with the characteristics of hydrological data, the rationality of the method is corroborated in hydrological feature discovery and time-series dependence extraction. Moreover, we tested the performance of the model on two constructed noise datasets to demonstrate its robustness. The results of all the above experiments show that the attention module significantly improves the learning and generalization ability, enhances noise resistance, and strengthens the robustness of the model compared with the traditional method. [ABSTRACT FROM AUTHOR]