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Purpose: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians., Methods: A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs., Results: At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680-0.720), 17.73 mm (95% CI: 16.08-19.39), and 3.11 mm (95% CI: 2.67-3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm)., Conclusions: The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation., (© 2024. The Author(s).)

In traditional image processing, the Fourier transform is often used to transform an image from the spatial domain to the frequency domain, and frequency filters are designed from the perspective of the frequency domain to sharpen or blur the image. In the field of remote sensing change detection, deep learning is beginning to become a mainstream tool. However, deep learning can still refer to traditional methodological ideas. In this paper, we designed a new convolutional neural network (MFGFNet) in which multiple global filters (GFs) are used to capture more information in the frequency domain, thus sharpening the image boundaries and better preserving the edge information of the change region. In addition, in MFGFNet, we use CNNs to extract multi-scale images to enhance the effects and to better focus on information about changes in different sizes (multi-scale combination module). The multiple pairs of enhancements are fused by the difference method and then convolved and concatenated several times to obtain a better difference fusion effect (feature fusion module). In our experiments, the IOUs of our network for the LEVIR-CD, SYSU, and CDD datasets are 0.8322, 0.6780, and 0.9101, respectively, outperforming the state-of-the-art model and providing a new perspective on change detection. [ABSTRACT FROM AUTHOR]

Ocean fronts have an important influence on global ocean–atmosphere interactions and marine fishery. Hence, it is of great significance to obtain the positions of the ocean fronts. However, current ocean front detection research confronts two challenges: scarcity of labeled data and limitations of ocean front detection algorithms. To address these two problems, we have collected and labeled an ocean front data set and proposed a new deep learning model for ocean front detection. For concreteness, due to the weak edge property of the ocean fronts, we formulate ocean front detection as a weak edge identification problem and propose the weak edge identification network (WEIN) for ocean front detection. WEIN consists of four convolutional blocks. Each block has a side output layer used to detect front edges at a specific image representation level. The side outputs are then fused to predict (detect) the locations of the ocean fronts. In this work, we adopt two metrics to measure the experimental results, i.e., the $F_{1}$ -score and intersection over union (IoU). The experimental results with comparison to traditional and deep learning approaches demonstrate the superiority of WEIN for ocean front detection. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis is not possible and real-time information for emergency situations is restricted. To solve this problem, this paper proposes an on-board ship detection scheme that is based on the traditional constant false alarm rate (CFAR) method and lightweight deep learning. This scheme can be used by the SAR satellite on-board computing platform to achieve near real-time image processing and data transmission. First, we use CFAR to conduct the initial ship detection and then apply the You Only Look Once version 4 (YOLOv4) method to obtain more accurate final results. We built a ground verification system to assess the feasibility of our scheme. With the help of the embedded Graphic Processing Unit (GPU) with high integration, our method achieved 85.9% precision for the experimental data, and the experimental results showed that the processing time was nearly half that required by traditional methods. [ABSTRACT FROM AUTHOR]

The rapid growth of Internet technology renders intrusion detection an important research topic in the field of pattern recognition. Considering that traffic data relate to not only temporal information, but also attack frequency, this paper presents a novel deep learning framework termed the multi-frequency intrusion detection network (MF-Net). MF-Net regards the pattern of Internet traffic as a superposition of sequential data with various frequencies, and is able to recognize the multi-frequency nature of network traffic data. The core of MF-Net is the multi-frequency LSTM (MF-LSTM) and multi-frequency transformer (MF-Transformer) module, both of which consist of high-frequency and low-frequency layers. In comparison with other state-of-the-art approaches on 4 public datasets, namely UNSW-NB15, KDD Cup 99, NSL-KDD and CICIDS 2017, as well as an IPv6 traffic dataset we created, MF-Net has shown better result in both binary and multi-class classification, which demonstrates the superiority of MF-Net over other compared approaches on network traffic intrusion detection. [Display omitted] • MF-Net captures multiple frequencies features with both long-term and short-term dependencies. • Both MF-LSTM and MF-Transformer detect network intrusion data with different frequencies. • MF-Net achieve state-of-the-art performance on 5 network intrusion datasets. [ABSTRACT FROM AUTHOR]

Highlights • The health state inversion method for Jack-up structure is established. • Deep Belief Network is investigated to extract the feature of the non-stationary output signal. • Structural safety inversion test is carried out considering both punch-through condition and structural damage condition. • The improved inversion method has higher identification accuracy in health state inversion. Abstract How to detect structural damages in large offshore structures such as Jack-up platform, is always an essential engineering problem. In this study, a novel discrete inversion approach is proposed to identify health state in various conditions. In order to solve the ill-posed problems in inverse problem, Deep Belief Network is investigated to extract the feature of the non-stationary output signal. Furthermore, structural safety inversion experiment is designed to demonstrate the effectiveness of the proposed approach, and sensor layout optimization is formulated in terms of Modal Assurance Criterion and Fisher information matrix. The results show that the features of structural response can be learned in the Deep Belief Network, and the signal features which are beneficial to state recognition can be extracted adaptively. The improved inversion method has higher prediction accuracy in health state inversion. [ABSTRACT FROM AUTHOR]