Mammogram density classification is a critical component of breast cancer screening, as breast density is a well-established risk factor that also impacts the sensitivity of mammographic imaging. Traditional deep learning (DL) approaches, such as convolutional neural network (CNN) based models, have shown limitations in this domain, often struggling with poor inter-class differentiation and lacking the ability to leverage relational context between different mammographic views. To address these challenges, we propose a two-part framework for mammogram density assessment. The first component, Multi-View Mammogram Density Classification using Contrastive Language-Image Pretraining (MMDC-CLIP), combines the representational strength of vision–language models with multi-view fusion. Semantic prompts are used to inject domain-specific priors, enhancing feature discrimination, while randomized data augmentation mitigates the challenges of limited annotated datasets. The second component, a Multi-View Auxiliary Confidence Network (MV-ACN), processes the final hidden states from all views through a multi-head attention mechanism to generate calibrated confidence scores, enabling reliable identification of uncertain cases that may require secondary review. Together, MMDC-CLIP and MV-ACN form the proposed MMDC-CLIP-F framework. The MMDC-CLIP classifier using the CLIP ViT-L/14-336 backbone reaches 78.2% accuracy, 5.9 percentage points higher than MV-DEFEAT, and 91.5% multi-class AUC, an 8.9-percentage-point improvement, on the RSNA-SMBC dataset. MV-ACN further provides calibrated uncertainty estimates; when paired with MMDC-CLIP using the CLIP ViT-B/32 backbone, confidence stratification on RSNA-SMBC yields 93.9% accuracy in high-confidence samples compared with 52.4% in low-confidence samples. These calibrated confidence estimates enable downstream decision support, such as deferring low-reliability cases for radiologist review, thereby improving the safety and interpretability of automated mammogram density assessment.

Recently, Moore-Penrose inverse (MPI)-based parameter fine-tuning of fully connected (FC) layers in pretrained deep convolutional neural networks (DCNNs) has emerged within the inductive transfer learning (ITL) paradigm. However, this approach has not gained significant traction in practical applications due to its stringent computational requirements. This work addresses this issue through a novel fast retraining strategy that enhances applicability of the MPI-based ITL. Specifically, during each retraining epoch, a random layer freezing protocol is utilized to manage the number of layers undergoing feature refinement. Additionally, this work incorporates an MPI-based approach for refining the trainable parameters of FC layers under batch processing, contributing to expedited convergence. Extensive experiments on several ImageNet pretrained benchmark DCNNs demonstrate that the proposed ITL achieves competitive performance with excellent convergence speed compared to conventional ITL methods. For instance, the proposed strategy converges nearly 1.5 times faster than retraining the ImageNet pretrained ResNet-50 using stochastic gradient descent with momentum (SGDM).

Autoencoders (AEs) have attracted much attention in one-class classification (OCC) based unsupervised anomaly detection. The AEs aim to learn the unity features on targets without involving anomalies and thus the targets are expected to obtain smaller reconstruction errors than anomalies. However, AE-based OCC algorithms may suffer from the overgeneralization of AE and fail to detect anomalies that have similar distributions to target data. To address these issues, a novel within-class constraint based multi-task AE (WC-MTAE) is proposed in this paper. WC-MTAE consists of two different task: one for reconstruction and the other for the discrimination-based OCC task. In this way, the encoder is compelled by the OCC task to learn the more compact encoded feature distribution for targets when minimizing OCC loss. Meanwhile, the within-class scatter based penalty term is constructed to further regularize the encoded feature distribution. The aforementioned two improvements enable the unsupervised anomaly detection by the compact encoded features, thereby addressing the issue of the overgeneralization in AEs. Comparisons with several state-of-the-art (SOTA) algorithms on several non-image datasets and an image dataset CIFAR10 are provided where the WC-MTAE is conducted on 3 different network structures including the multilayer perception (MLP), LeNet-type convolution network and full convolution neural network. Extensive experiments demonstrate the superior performance of the proposed WC-MTAE. The source code would be available in future.

The broad learning system (BLS) is a versatile and effective tool for analyzing tabular data. However, the rapid expansion of big data has resulted in an overwhelming amount of tabular data, necessitating the development of specialized tools for effective management and analysis. This article introduces an optimized BLS (OBLS) specifically tailored for big data analysis. In addition, a deep-optimized BLS (DOBLS) network is developed further to enhance the performance and efficiency of the OBLS. The main contributions of this article are: 1) by retracing the network's error from the output space to the latent space, the OBLS adjusts parameters in the feature and enhancement node layers. This process aims to achieve more resilient representations, resulting in improved performance; 2) the DOBLS is a multilayered structure consisting of multiple OBLSs, wherein each OBLS connects to the input and output layers, enabling direct data propagation. This design helps reduce information loss between layers, ensuring an efficient flow of information throughout the network; and 3) the proposed methods demonstrate robustness across various applications, including multiview feature embedding, one-class classification (OCC), camera model identification, electroencephalogram (EEG) signal processing, and radar signal analysis. Experimental results validate the effectiveness of the proposed models. To ensure reproducibility, the source code is available at https://github.com/1027051515/OBLS_DOBLS.

The need for a reliable system to detect high-risk incidents in complex settings like roadways, which are infrequent but potentially dangerous, has arisen due to the occurrence of rare hazardous events. This system would empower self-driving cars to function autonomously over extended periods without human involvement. Among these hazardous occurrences, accidents have received the least attention due to their rarity and diverse nature. Recently, dashboard cameras (dashcams) have gained recognition in academic circles as a cost-effective and accessible solution to enhance the safety of autonomous vehicles when handling accidents, since they are now commonly found in most vehicles. This review presents the progression of concepts in this domain, tracing its development from early ideas to cutting-edge techniques. It categorizes these approaches into supervised, self-supervised, and unsupervised learning. Furthermore, the review thoroughly examines evaluation criteria and available datasets, providing a comprehensive comparison of the strengths and limitations of different methods. Ultimately, the review proposes potential avenues for future research in this field.

High-frequency surface wave radar (HFSWR) is a powerful tool for ship detection and surveillance. blackHowever, the use of pre-trained deep learning (DL) networks for ship detection is challenging due to the limited training samples in HFSWR and the substantial differences between remote sensing images and everyday images. To tackle these issues, this paper proposes a coarse-to-fine target detection approach that combines traditional methods with DL, resulting in improved performance. The contributions of this work include: 1) a two-stage learning pipeline that integrates spatial-frequency analysis (SFA) with subnet-based neural networks, 2) an automatic linear thresholding algorithm for plausible target region (PTR) detection, and 3) a robust subnet neural network for fine target detection. The advantage of using SFA and subnet network is that the SFA reduces the need for extensive training data, while the subnet neural network excels at localizing ships even with limited training data. Experimental results on the HFSWR-RD dataset affirm the model's superior performance compared to rival algorithms.

Multilayer analytic learning plays a crucial role in data mining and representation learning. Nevertheless, most of them encounter inefficiencies in latent space encoding, resulting in less effective data representations. Aimed at addressing this limitation, this paper introduces two potent analytic learning methods, the progressive learning-based hierarchical subnet neural network (P-HSNN) and the robust P-HSNN (RP-HSNN). The contributions are as follows. First, two progressive learning astrategies based on subnetwork nodes are proposed. Second, the RP-HSNN is a Laplacian matrix-based algorithm, where label information and input representations are utilized simultaneously to optimize the subspace feature. Third, the dimension of subnetwork node is gradually increased. The global-level representation is formed by combining the features from the subnetworks. The model's convergence is thoroughly demonstrated through rigorous mathematical proof. Experimental analyses across various domains, spanning a wide range of training samples from 2,754 to 1,623,114, confirm the superior performance of the proposed algorithms over state-of-the-art multilayer analytic learning methods.

Most multilayer Moore-Penrose inverse (MPI)-based neural networks, such as deep random vector functional link (RVFL), are structured with two separate stages: unsupervised feature encoding and supervised pattern classification. Once the unsupervised learning is finished, the latent encoding is fixed without supervised fine-tuning. However, in complex tasks such as handling the ImageNet dataset, there are often many more clues that can be directly encoded, while unsupervised learning, by definition, cannot know exactly what is useful for a certain task. There is a need to retrain the latent space representations in the supervised pattern classification stage to learn some clues that unsupervised learning has not yet been learned. In particular, the residual error in the output layer is pulled back to each hidden layer, and the parameters of the hidden layers are recalculated with MPI for more robust representations. In this article, a recomputation-based multilayer network using Moore-Penrose inverse (RML-MP) is developed. A sparse RML-MP (SRML-MP) model to boost the performance of RML-MP is then proposed. The experimental results with varying training samples (from 3k to 1.8 million) show that the proposed models provide higher Top-1 testing accuracy than most representation learning algorithms. For reproducibility, the source codes are available at https://github.com/W1AE/Retraining.

Multilayer least-square (LS)-based one-class classification networks (MLS-OCNs) have gained great attention for the purpose of identifying anomalies and outliers. However, many MLS-OCNs encounter the issue of loosely connected feature coding because they use two separate mechanisms for feature encoding and final pattern recognition. This paper proposes a solution to this problem by introducing a multilayer algorithm called deep Moore-Penrose inverse network with refinement (DMPINR). In particular, DMPINR employs an end-to-end learning process based on the Moore-Penrose inverse (MPI) to identify optimal latent space and classify objects simultaneously. To enhance the robustness of representations, the DMPINR technique pulls back the residual error from the output layer to the hidden layers sequentially, recalculating the parameters of these hidden layers using MPI. The experimental results on ten popular OCC datasets demonstrate that the proposed approach outperforms many existing MLS-OCNs in G-Mean and F1scores.

Semisupervised classification with a few labeled training samples is a challenging task in the area of data mining. Moore-Penrose inverse (MPI)-based manifold regularization (MR) is a widely used technique in tackling semisupervised classification. However, most of the existing MPI-based MR algorithms can only generate loosely connected feature encoding, which is generally less effective in data representation and feature learning. To alleviate this deficiency, we introduce a new semisupervised multilayer subnet neural network called SS-MSNN. The key contributions of this article are as follows: 1) a novel MPI-based MR model using the subnetwork structure is introduced. The subnet model is utilized to enrich the latent space representations iteratively; 2) a one-step training process to learn the discriminative encoding is proposed. The proposed SS-MSNN learns parameters by directly optimizing the entire network, accepting input from one end, and producing output at the other end; and 3) a new semisupervised dataset called HFSWR-RDE is built for this research. Experimental results on multiple domains show that the SS-MSNN achieves promising performance over the other semisupervised learning algorithms, demonstrating fast inference speed and better generalization ability.

The multilayer one-class classification (OCC) frameworks have gained great traction in research on anomaly and outlier detection. However, most multilayer OCC algorithms suffer from loosely connected feature coding, affecting the ability of generated latent space to properly generate a highly discriminative representation between object classes. To alleviate this deficiency, two novel OCC frameworks, namely: 1) OCC structure using the subnetwork neural network (OC-SNN) and 2) maximum correntropy-based OC-SNN (MCOC-SNN), are proposed in this article. The novelties of this article are as follows: 1) the subnetwork is used to build the discriminative latent space; 2) the proposed models are one-step learning networks, instead of stacking feature learning blocks and final classification layer to recognize the input pattern; 3) unlike existing works which utilize mean square error (MSE) to learn low-dimensional features, the MCOC-SNN uses maximum correntropy criterion (MCC) for discriminative feature encoding; and 4) a brand-new OCC dataset, called CO-Mask, is built for this research. Experimental results on the visual classification domain with a varying number of training samples from 6131 to 513 061 demonstrate that the proposed OC-SNN and MCOC-SNN achieve superior performance compared to the existing multilayer OCC models. For reproducibility, the source codes are available at https://github.com/W1AE/OCC.

A high-frequency surface wave radar (HFSWR) is an effective tool for monitoring an exclusive economic zone (EEZ). However, the presence of diverse clutters and noises that contaminate the echo signals of the radar hinder its maritime surveillance. To address this issue, this paper presents a two-stage hierarchical one-class classification network (HOCN) designed specifically for ship-target detection in range-Doppler (RD) images. In Stage 1, the plausible region of interest (PROI) is extracted. This stage employs a dynamic threshold optimization strategy and Laplacian kernel to identify the potential regions of interest. In Stage 2, the proposed one-class deconvolutional-and-convolutional network (OC-DCNet) is utilized for fine detection of ship-targets. This stage comprises two sub-modules: the deconvolutional sub-module, which expands the input into a 2D matrix, and the convolutional sub-module, which classifies the input pattern as either a ship-target or a non-ship-target. The experimental results on a newly collected dataset called HFRD demonstrate the effectiveness of the proposed HFSWR ship-target detection algorithm.

High-frequency surface wave radar (HFSWR) can be effectively used to detect ships in the exclusive economic zone. However, the ship signal is concealed and interfered with various clutter and background noise in the Doppler spectrum. In this letter, a range-Doppler (RD) image-based novel ship detection algorithm is proposed by exploiting spatial-frequency information and a unique feature fusion based on the analysis of variance. The algorithm subsumes three successive stages: Stage I—the plausible region of interest is captured, Stage II—the features from different sources are fused into one generalized feature space, and Stage III—an extreme learning machine-based classifier is utilized to localize the ships. Experimental results on challenging HFSWR-RD datasets demonstrate that the proposed algorithm has a competitive performance over other ship detection algorithms.

High-frequency surface wave radar (HFSWR) has become the cornerstone of maritime surveillance because of its low-cost maintenance and coverage of wide area. However, when it comes to the extraction of key areas, such as vessel-target detection and vessel-path tracking, the HFSWR signal is strongly interfered by clutters and noise, which makes maritime surveillance a challenging task. This article proposes a hierarchical key-area perception model for maritime surveillance harnessing range-Doppler (RD) image from HFSWR, Laplacian kernel, a linear classifier (LC), and a subnet-based multilayer representation learning framework (SMRLF). First, a weak LC with a Laplacian kernel is utilized to capture the plausible vessel regions (PVRs). Then, a novel SMRLF is proposed to localize the vessel targets from the PVRs. To handle the noise, a maximum correntropy criterion with variable centers (MCC-VC) is incorporated in the subnet-based learning model. A thorough experimental analysis on cross-domain samples from radar dataset to scene classification dataset shows that the proposed HKPM performs competitively. The model shows a superior performance over most of the state-of-the-art vessel-target detection algorithms with a vessel-target detection accuracy of 94%. The extended analysis on image classification problem proves that the proposed model has great adaptivity and scalability.

This article presents a new supervised multilayer subnetwork-based feature refinement and classification model for representation learning. The novelties of this algorithm are as follows: 1) different from most multilayer networks that go deeper with increased number of network layers, this work architects a model with wider subnetwork nodes; 2) the conventional classification methods adopt a separate search mechanism to derive a generalized feature space and to get the final cognition, but this work proposes a one-shot process to find the meaningful latent space and recognize the objects; and 3) the traditional feature representation and image classification approaches apply a unimodal feature coding, which suffers from lack of global knowledge. This work overcomes the pitfall through multimodal fusion that fuses various feature sources into one superstate encoding to achieve higher performance. A cross-domain experimental study on camera identification and image classification shows that the proposed method achieves superior performance compared to the existing models.

Fully connected representation learning (FCRL) is one of the widely used network structures in multimodel image classification frameworks. However, most FCRL-based structures, for instance, stacked autoencoder encode features and find the final cognition with separate building blocks, resulting in loosely connected feature representation. This article achieves a robust representation by considering a low-dimensional feature and the classifier model simultaneously. Thus, a new hierarchical subnetwork-based neural network (HSNN) is proposed in this article. The novelties of this framework are as follows: 1) it is an iterative learning process, instead of stacking separate blocks to obtain the discriminative encoding and the final classification results. In this sense, the optimal global features are generated; 2) it applies Moore-Penrose (MP) inverse-based batch-by-batch learning strategy to handle large-scale data sets, so that large data set, such as Place365 containing 1.8 million images, can be processed effectively. The experimental results on multiple domains with a varying number of training samples from ∼ 1 K to ∼ 2 M show that the proposed feature reinforcement framework achieves better generalization performance compared with most state-of-the-art FCRL methods.

High-frequency surface wave radar (HFSWR) has a vital civilian and military significance for continuous maritime surveillance of activities within exclusive economic zone. However, HFSWR has lower spatial and temporal resolutions and the received signals are strongly polluted by different clutter and background noise. Therefore, ship target detection by HFSWR has become a challenging task. This letter presents an automatic ship target detection algorithm based on discrete wavelet transform (DWT). First, a peak signal-to-noise ratio-based algorithm is proposed to automatically determine the optimal scale of DWT for extraction of ship targets. Second, the high-frequency coefficients of DWT at the optimal scale are processed by a fuzzy set-based method to enhance the useful target information and depress the unwanted background noises. Third, a target-highlighted image is reconstructed by ignoring all the low-frequency coefficients and performing inverse DWT only to the enhanced high-frequency coefficients. Finally, the targets are extracted by adaptive threshold segmentation of the final target-highlighted image. Experimental results show that the proposed approach can automatically extract ship targets effectively for range Doppler images with complex background, and has a better target detection performance than the previous wavelet-based algorithm, thereby providing a new reliable image processing-based method of ship target detection for HFSWR.