Classification Module Research Articles

Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model

The rotating parts of large and complex equipment are key components that ensure the normal operation of the equipment. Accurate fault diagnosis is crucial for the safe operation of these systems. To simultaneously extract both local and global valuable fault feature information from key components of complex equipment, this study proposes a fault diagnosis network model, named MultiDilatedFormer, which is based on the fusion of transformer and multi-head dilated convolution. The newly designed multi-head dilated convolution module is sequentially integrated into the transformer-encoder architecture, constructing a feature extraction module where the complementary advantages of both components enhance overall performance. Firstly, the sample is expanded into a two-dimensional feature map and then input into the newly designed feature extraction module. Finally, the diagnostic output is performed by the designed patch feature fusion module and classifier module. Additionally, interpretability research is conducted on the proposed model, aiming to understand the decision-making mechanism of the model through visual analysis of the entire decision process. The experimental results on three different datasets indicate that the proposed model achieved high accuracy in fault diagnosis with relatively short data windows. The highest accuracy reached 97.95%, which was up to 10.97% higher than other models. Furthermore, the feasibility of the model is also verified in the actual dataset of the rotating parts of the injection molding machine. The excellent performance of the model on different datasets demonstrates its effectiveness in extracting comprehensive fault feature information and also proves its great potential in practical industrial applications.

Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays

Early and precise diagnosis of craniosynostosis (CSO), which involves premature fusion of cranial sutures in infants, is crucial for effective treatment. Although computed topography offers detailed imaging, its high radiation poses risks, especially to children. Therefore, we propose a deep-learning model for CSO and suture-line classification using 2D cranial X-rays that minimises radiation-exposure risks and offers reliable diagnoses. We used data comprising 1,047 normal and 277 CSO cases from 2006 to 2023. Our approach integrates X-ray-marker removal, head-pose standardisation, skull-cropping, and fine-tuning modules for CSO and suture-line classification using convolution neural networks (CNNs). It enhances the diagnostic accuracy and efficiency of identifying CSO from X-ray images, offering a promising alternative to traditional methods. Four CNN backbones exhibited robust performance, with F1-scores exceeding 0.96 and sensitivity and specificity exceeding 0.9, proving the potential for clinical applications. Additionally, preprocessing strategies further enhanced the accuracy, demonstrating the highest F1-scores, precision, and specificity. A qualitative analysis using gradient-weighted class activation mapping illustrated the focal points of the models. Furthermore, the suture-line classification model distinguishes five suture lines with an accuracy of > 0.9. Thus, the proposed approach can significantly reduce the time and labour required for CSO diagnosis, streamlining its management in clinical settings.

Two-Stream Modality-Based Deep Learning Approach for Enhanced Two-Person Human Interaction Recognition in Videos.

Human interaction recognition (HIR) between two people in videos is a critical field in computer vision and pattern recognition, aimed at identifying and understanding human interaction and actions for applications such as healthcare, surveillance, and human-computer interaction. Despite its significance, video-based HIR faces challenges in achieving satisfactory performance due to the complexity of human actions, variations in motion, different viewpoints, and environmental factors. In the study, we proposed a two-stream deep learning-based HIR system to address these challenges and improve the accuracy and reliability of HIR systems. In the process, two streams extract hierarchical features based on the skeleton and RGB information, respectively. In the first stream, we utilised YOLOv8-Pose for human pose extraction, then extracted features with three stacked LSM modules and enhanced them with a dense layer that is considered the final feature of the first stream. In the second stream, we utilised SAM on the input videos, and after filtering the Segment Anything Model (SAM) feature, we employed integrated LSTM and GRU to extract the long-range dependency feature and then enhanced them with a dense layer that was considered the final feature for the second stream module. Here, SAM was utilised for segmented mesh generation, and ImageNet was used for feature extraction from images or meshes, focusing on extracting relevant features from sequential image data. Moreover, we newly created a custom filter function to enhance computational efficiency and eliminate irrelevant keypoints and mesh components from the dataset. We concatenated the two stream features and produced the final feature that fed into the classification module. The extensive experiment with the two benchmark datasets of the proposed model achieved 96.56% and 96.16% accuracy, respectively. The high-performance accuracy of the proposed model proved its superiority.

Vocal cord anomaly detection based on Local Fine-Grained Contour Features

Laryngoscopy is a popular examination for vocal cord disease diagnosis. The conventional screening of laryngoscopic images is labor-intensive and depends heavily on the experience of the medical specialists. Automatic detection of vocal cord diseases from laryngoscopic images is highly sought to assist regular image reading. In laryngoscopic images, the symptoms of vocal cord diseases are concentrated in the inner vocal cord contour, which is often characterized as vegetation and small protuberances. The existing classification methods pay little, if any, attention to the role of vocal cord contour in the diagnosis of vocal cord diseases and fail to effectively capture the fine-grained features. In this paper, we propose a novel Local Fine-grained Contour Feature extraction method for vocal cord anomaly detection. Our proposed method consists of four stages: image segmentation to obtain the overall vocal cord contour, inner vocal cord contour isolation to obtain the inner contour curve by comparing the changes of adjacent pixel values, extraction of the latent feature in the inner vocal cord contour by taking the tangent inclination angle of each point on the contour as the latent feature, and the classification module. Our experimental results demonstrate that the proposed method improves the detection performance of vocal cord anomaly and achieves an accuracy of 97.21%.

Attention-based CNN model for motor imagery classification from nonlinear EEG signals

Motor imagery (MI)-based brain-computer interface (BCI) provides a promising solution for the limb rehabilitation of stroke patients. For better rehabilitation performance, high-precision classification of MI-related EEG signals plays a critical role. However, this is still a challenging problem for multi-category MI signals. In this paper, we focus on four commonly used stroke rehabilitation actions, and propose a modular temporal-spatial attention-based CNN (MTSACNN) model for MI classification. In detail, we carry out the MI experiments and acquire the EEG signals related to imagining left/right fist clenching and left/right wrist dorsiflexion. MTSACNN model firstly extracts the low-order MI features through the temporal-spatial feature extraction module (TSFE module). Especially, a group attention mechanism is proposed for intra-group information interaction. Secondly, considering the short- and long-term working characteristics of brain, high-order temporal features are further extracted and fused by the multi-level feature fusion module (MLFF module). Finally, four auxiliary losses are arranged in the classification module (C module) to speed up the model optimization process. The experimental results show that MTSACNN model can achieve good performance in decoding rehabilitation-related MI brain intentions, achieving an average classification accuracy of 72.05% for fourteen subjects. This work is beneficial to promote the construction of high-performance stroke rehabilitation BCI system.

Grounded situation recognition under data scarcity

Grounded Situation Recognition (GSR) aims to generate structured image descriptions. For a given image, GSR needs to identify the key verb, the nouns corresponding to roles, and their bounding-box groundings. However, current GSR research demands numerous meticulously labeled images, which are labor-intensive and time-consuming, making it costly to expand detection categories. Our study enhances model accuracy in detecting and localizing under data scarcity, reducing dependency on large datasets and paving the way for broader detection capabilities. In this paper, we propose the Grounded Situation Recognition under Data Scarcity (GSRDS) model, which uses the CoFormer model as the baseline and optimizes three subtasks: image feature extraction, verb classification, and bounding-box localization, to better adapt to data-scarce scenarios. Specifically, we replace ResNet50 with EfficientNetV2-M for advanced image feature extraction. Additionally, we introduce the Transformer Combined with CLIP for Verb Classification (TCCV) module, utilizing features extracted by CLIP’s image encoder to enhance verb classification accuracy. Furthermore, we design the Multi-source Verb-Role Queries (Multi-VR Queries) and the Dual Parallel Decoders (DPD) modules to improve the accuracy of bounding-box localization. Through extensive comparative experiments and ablation studies, we demonstrate that our method achieves higher accuracy than mainstream approaches in data-scarce scenarios. Our code will be available at https://github.com/Zhou-maker-oss/GSRDS.

A hybrid feature fusion deep learning framework for multi-source medical image analysis

Despite the widespread adoption of deep learning to enhance image classification, significant obstacles remain. First, multisource data with diverse sizes and formats is a great challenge for most current deep learning models. Second, lacking manual labeled data for model training limits the application of deep learning. Third, the widely used CNN-based methods shows their limitations in extracting global features and yield poor performance for image topology. To address these issues, we propose a Hybrid Feature Fusion Deep Learning (HFFDL) framework for image classification. This framework consists of an automated image segmentation module, a two-stream backbone module, and a classification module. The automatic image segmentation module utilizes the U-Net model and transfer learning to detect region of interest (ROI) in multisource images; the two-stream backbone module integrates the Swin Transformer architecture with the Inception CNN, with the aim of simultaneous extracting local and global features for efficient representation learning. We evaluate the performance of HFFDL framework with two publicly available image datasets: one for identifying COVID-19 through X-ray scans of the chest (30,386 images), and another for multiclass skin cancer screening using dermoscopy images (25,331 images). The HFFDL framework exhibited greater performance in comparison to many cutting-edge models, achieving the AUC score 0.9835 and 0.8789, respectively. Furthermore, a practical application study conducted in a hospital, identifying viable embryos using medical images, revealed the HFFDL framework outperformed embryologists.

SwinUNeCCt: bidirectional hash-based agent transformer for cervical cancer MRI image multi-task learning

Cervical cancer is the fourth most common malignant tumor among women globally, posing a significant threat to women’s health. In 2022, approximately 600,000 new cases were reported, and 340,000 deaths occurred due to cervical cancer. Magnetic resonance imaging (MRI) is the preferred imaging method for diagnosing, staging, and evaluating cervical cancer. However, manual segmentation of MRI images is time-consuming and subjective. Therefore, there is an urgent need for automatic segmentation models to identify cervical cancer lesions in MRI scans accurately. All MRIs in our research are from cervical cancer patients diagnosed by pathology at Tongren City People’s Hospital. Strict data selection criteria and clearly defined inclusion and exclusion conditions were established to ensure data consistency and accuracy of research results. The dataset contains imaging data from 122 cervical cancer patients, with each patient having 100 pelvic dynamic contrast-enhanced MRI scans. Annotations were jointly completed by medical professionals from Universiti Putra Malaysia and the Radiology Department of Tongren City People’s Hospital to ensure data accuracy and reliability. Additionally, a novel computer-aided diagnosis model named SwinUNeCCt is proposed. This model incorporates (i) A bidirectional hash-based agent multi-head self-attention mechanism, which optimizes the interaction between local and global features in MRI, aiding in more accurate lesion identification. (ii) Reduced computational complexity of the self-attention mechanism. The effectiveness of the SwinUNeCCt model has been validated through comparisons with state-of-the-art 3D medical models, including nnUnet, TransBTS, nnFormer, UnetR, UnesT, SwinUNetR, and SwinUNeLCsT. In semantic segmentation tasks without a classification module, the SwinUNeCCt model demonstrates excellent performance across multiple key metrics: achieving a 95HD of 6.25, an IoU of 0.669, and a DSC of 0.802, all of which are the best results among the compared models. Simultaneously, SwinUNeCCt strikes a good balance between computational efficiency and model complexity, requiring only 442.7 GFLOPs of computational power and 71.2 M parameters. Furthermore, in semantic segmentation tasks that include a classification module, the SwinUNeCCt model also exhibits powerful recognition capabilities. Although this slightly increases computational overhead and model complexity, its performance surpasses other comparative models. The SwinUNeCCt model demonstrates excellent performance in semantic segmentation tasks, achieving the best results among state-of-the-art 3D medical models across multiple key metrics. It balances computational efficiency and model complexity well, maintaining high performance even with the inclusion of a classification module.

Modified RESNET50 with Attention Module for Detection and Classification of Pests in Vegetable Crops

The agricultural sector plays a pivotal role in ensuring global food security. This study addresses the significant challenge of pest infestations in vegetable crops by automating pest identification and classification through deep learning techniques. We utilize a state-of-the-art Convolutional Neural Network (CNN), specifically a Modified ResNet50 architecture enhanced with an Adversarial Attention Module. This approach is designed to improve feature extraction and model performance. The purpose of our study is to develop and evaluate a model that can accurately identify and classify pest species, thereby aiding in timely pest management. The ResNet50 backbone, pre-trained on an extensive dataset of pest-crop interactions, is augmented with an attention module to refine its capabilities. Performance is evaluated on a hold-out dataset of previously unseen images, where the Modified ResNet50 achieves a classification accuracy of [insert quantitative result, e.g., 92%]. Comparative analysis shows that our model outperforms other deep learning models by [insert quantitative result, e.g., 5%] in precision and recall metrics. This research contributes to precision agriculture by offering a more effective and environmentally friendly pest identification solution, which supports improved pest management strategies and enhances global food security.

ElectrApp: Deep learning framework to enhance cardiac arrhythmia detection

Abstract Medical specialists who diagnose patients with cardiovascular arrhythmias are not promptly available in rural areas. The proposed information system provides access to upload the data to a cloud-based processing module in which different arrhythmias are classified, allowing the classification of various arrhythmias to solve the scarcity of physicians in remote areas through an automatic system that helps with the diagnosis. This information system has a controller module for the operation that manages data collection, processing, beautification and visualization tasks. We used the MIT-BIH Arrhythmia Database during the training and validation of the classification module. The module uses deep learning to identify five different types of heartbeats according to standards, reporting 98.9% accuracy. The information system will improve health coverage in the prediagnosis of cardiovascular diseases.

A divide-and-conquer reconstruction method for defending against adversarial example attacks

In recent years, defending against adversarial examples has gained significant importance, leading to a growing body of research in this area. Among these studies, pre-processing defense approaches have emerged as a prominent research direction. However, existing adversarial example pre-processing techniques often employ a single pre-processing model to counter different types of adversarial attacks. Such a strategy may miss the nuances between different types of attacks, limiting the comprehensiveness and effectiveness of the defense strategy. To address this issue, we propose a divide-and-conquer reconstruction pre-processing algorithm via multi-classification and multi-network training to more effectively defend against different types of mainstream adversarial attacks. The premise and challenge of the divide-and-conquer reconstruction defense is to distinguish between multiple types of adversarial attacks. Our method designs an adversarial attack classification module that exploits the high-frequency information differences between different types of adversarial examples for their multi-classification, which can hardly be achieved by existing adversarial example detection methods. In addition, we construct a divide-and-conquer reconstruction module that utilizes different trained image reconstruction models for each type of adversarial attack, ensuring optimal defense effectiveness. Extensive experiments show that our proposed divide-and-conquer defense algorithm exhibits superior performance compared to state-of-the-art pre-processing methods.

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis

Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.

TAENet: Two-branch Autoencoder Network for Interpretable Deepfake Detection

Deepfake detection attracts increasingly attention due to serious security issues caused by facial manipulation techniques. Recently, deep learning-based detectors have achieved promising performance. However, these detectors suffer severe untrustworthy due to the lack of interpretability. Thus, it is essential to work on the interpretibility of deepfake detectors to improve the reliability and traceability of digital evidence. In this work, we propose a two-branch autoencoder network named TAENet for interpretable deepfake detection. TAENet is composed of Content Feature Disentanglement (CFD), Content Map Generation (CMG), and Classification. CFD extracts latent features of real and forged content with dual encoder and feature discriminator. CMG employs a Pixel-level Content Map Generation Loss (PCMGL) to guide the dual decoder in visualizing the latent representations of real and forged contents as real-map and fake-map. In classification module, the Auxiliary Classifier (AC) serves as map amplifier to improve the accuracy of real-map image extraction. Finally, the learned model decouples the input image into two maps that have the same size as the input, providing visualized evidence for deepfake detection. Extensive experiments demonstrate that TAENet can offer interpretability in deepfake detection without compromising accuracy.

Few-shot learning based histopathological image classification of colorectal cancer

Background Colorectal cancer is a prevalent and deadly disease worldwide, posing significant diagnostic challenges. Traditional histopathologic images classification is often inefficient and subjective. Although some histopathologists use computer-aided diagnosis (CAD) to improve efficiency, these methods depend heavily on extensive data and specific annotations, limiting their development. To address these challenges, this paper proposes a method based on few-shot learning. Methods This study introduces a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories. The model comprises modules for feature extraction, dimensionality reduction, and classification, trained using a combination of contrast loss and cross-entropy loss. In this paper, we detail the setup of hyperparameters:n-way, k-shot, β and the creation of support, query, and test datasets. Results Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category. We documented the model’s loss, accuracy, and the confusion matrix of the results. Additionally, we employed the t-SNE algorithm to analyze and assess the model’s classification performance. Conclusion The proposed model demonstrates significant advantages in accuracy and minimal data dependency, performing robustly across all tested n-way, k-shot scenarios. It consistently achieved over 93% accuracy on comprehensive test datasets, including 1916 samples, confirming its high classification accuracy and strong generalization capability. Our research advances the use of few-shot learning in medical diagnostics, and also lays the groundwork for extending it to deal with rare, difficult-to-diagnose cases.

Multi-Modal Fusion Network with Multi-Head Self-Attention for Injection Training Evaluation in Medical Education

The COVID-19 pandemic has significantly disrupted traditional medical training, particularly in critical areas such as the injection process, which require expert supervision. To address the challenges posed by reduced face-to-face interactions, this study introduces a multi-modal fusion network designed to evaluate the timing and motion aspects of the injection training process in medical education. The proposed framework integrates 3D reconstructed data and 2D images of hand movements during the injection process. The 3D data are preprocessed and encoded by a Long Short-Term Memory (LSTM) network to extract temporal features, while a Convolutional Neural Network (CNN) processes the 2D images to capture detailed image features. These encoded features are then fused and refined through a proposed multi-head self-attention module, which enhances the model’s ability to capture and weigh important temporal and image dynamics in the injection process. The final classification of the injection process is conducted by a classifier module. The model’s performance was rigorously evaluated using video data from 255 subjects with assessments made by professional physicians according to the Objective Structured Assessment of Technical Skill—Global Rating Score (OSATS-GRS)[B] criteria for time and motion evaluation. The experimental results demonstrate that the proposed data fusion model achieves an accuracy of 0.7238, an F1-score of 0.7060, a precision of 0.7339, a recall of 0.7238, and an AUC of 0.8343. These findings highlight the model’s potential as an effective tool for providing objective feedback in medical injection training, offering a scalable solution for the post-pandemic evolution of medical education.

VQGNet: An Unsupervised Defect Detection Approach for Complex Textured Steel Surfaces

Defect detection on steel surfaces with complex textures is a critical and challenging task in the industry. The limited number of defect samples and the complexity of the annotation process pose significant challenges. Moreover, performing defect segmentation based on accurate identification further increases the task’s difficulty. To address this issue, we propose VQGNet, an unsupervised algorithm that can precisely recognize and segment defects simultaneously. A feature fusion method based on aggregated attention and a classification-aided module is proposed to segment defects by integrating different features in the original images and the anomaly maps, which direct the attention to the anomalous information instead of the irregular complex texture. The anomaly maps are generated more confidently using strategies for multi-scale feature fusion and neighbor feature aggregation. Moreover, an anomaly generation method suitable for grayscale images is introduced to facilitate the model’s learning on the anomalous samples. The refined anomaly maps and fused features are both input into the classification-aided module for the final classification and segmentation. VQGNet achieves state-of-the-art (SOTA) performance on the industrial steel dataset, with an I-AUROC of 99.6%, I-F1 of 98.8%, P-AUROC of 97.0%, and P-F1 of 80.3%. Additionally, ViT-Query demonstrates robust generalization capabilities in generating anomaly maps based on the Kolektor Surface-Defect Dataset 2.

A novel anomaly detection method for magnetic flux leakage signals via a feature-based unsupervised detection network

High-precision anomaly detection, as the key technology of magnetic flux leakage (MFL) signal detection, is a challenging task. It is difficult to detect anomalies in MFL signals due to the variety of anomalies and the characteristics of the anomalies are easily submerged in the variation of the natural signals. To address the above issues, a feature-based unsupervised detection network (FUDet) is designed, which accomplishes the unsupervised anomaly detection task through feature discrimination and feature reconstruction. Firstly, a bidirectional discrimination module is proposed, which can input normal and anomaly feature distributions to mine the characteristics of samples, so as to enhance the ability of the model to recognize anomaly signals. Secondly, a dynamic noise generation module is designed to generate different feature distributions for each input that are consistent with the characteristics of MFL signals. This module creates an adversarial effect with the discriminator, allowing it to identify more subtle feature differences through training. Finally, a reconstruction classification module is designed to naturally reconstruct the non-normal features and normal features into normal signals, which can be used to detect anomalies by comparing the difference between the input signals and the reconstructed signals. Experimentally, the method is proved to outperform the P-AUROC of the state-of-the-art method by 3.1% under MFL signals and achieves outstanding results in MFL signal anomaly detection.

Multi-dimensional weighted deep subspace clustering with feature classification

Deep subspace clustering (DSC) methods based on deep autoencoder (DAE) and self-expression layers have achieved impressive performance. However, traditional DSC method often loses useful information in the feature extraction of DAE, leading to incomplete learning of the self-expression coefficient matrix. Besides, existing deep subspace clustering methods always extract feature information from a single dimension, ignoring the potential information that may exist in the self-expression coefficient matrix across multiple dimensions. In this paper, we propose a novel method called multi-dimensional weighted deep subspace clustering based on feature classification (MWDSC). Specifically, we use the multivariate feature classification module (MFC) to learn the potential information of multiple local feature matrix for local pre-clustering. To further improve the clustering performance, we propose a self-flip weighted fusion strategy (SWF). The SWF strategy obtains a self-expression coefficient matrix with rich features through self-flipping weighting, thereby aiding in achieving global optimal solutions. Extensive experiments conducted on five benchmark datasets validate the superiority and effectiveness of our proposed method over other state-of-the-art approaches.

ToxGIN: an In silico prediction model for peptide toxicity via graph isomorphism networks integrating peptide sequence and structure information.

Peptide drugs have demonstrated enormous potential in treating a variety of diseases, yet toxicity prediction remains a significant challenge in drug development. Existing models for prediction of peptide toxicity largely rely on sequence information and often neglect the three-dimensional (3D) structures of peptides. This study introduced a novel model for short peptide toxicity prediction, named ToxGIN. The model utilizes Graph Isomorphism Network (GIN), integrating the underlying amino acid sequence composition and the 3D structures of peptides. ToxGIN comprises three primary modules: (i) Sequence processing module, converting peptide 3D structures and sequences into information of nodes and edges; (ii) Feature extraction module, utilizing GIN to learn discriminative features from nodes and edges; (iii) Classification module, employing a fully connected classifier for toxicity prediction. ToxGIN performed well on the independent test set with F1 score = 0.83, AUROC = 0.91, and Matthews correlation coefficient = 0.68, better than existing models for prediction of peptide toxicity. These results validated the effectiveness of integrating 3D structural information with sequence data using GIN for peptide toxicity prediction. The proposed ToxGIN and data can be freely accessible at https://github.com/cihebiyql/ToxGIN.

UPPER CRETACEOUS–LOWER EOCENE MUDSTONES OF THE OUTER CARPATHIANS (PETROCHEMICAL AND PALEOGEODYNAMIC ASPECTS)

The mineral types of the Upper Cretaceous–Lower Eocene greenish-gray mudstones of the Skyba and Boryslav-Pokuttya nappes of the Outer Carpathians were studied. According to sedimentological features, these mudstones belong to hemipelagites and contain information about the background sedimentation conditions of the deep-sea oceanic basin. The purpose of the work is to clarify the paleogeodynamic features of the basin based on the results of studying the material composition of the Upper Cretaceous–Lower Eocene background mudstones, which in particular are part of the variegated-colored horizons. Research methods were geological mapping, X-ray structural, sedimentological, lithostratigraphic, petrogeochemical, geodynamic analysis. On the classification module diagram (Na2O+K2O)/Al2O3–(FeO*+MnO+MgO)/SiO2, the figurative points of the contents of the Upper Cretaceous–Lower Eocene mudstones of the Stryi, Yamna, and Manyava suites fall into the fields corresponding to two mineral types: a mixture with a predominance of montmorillonite and three-component mixture of chlorite+montmorillonite+hydromica composition. According to the obtained data, a gradual change in the conditions of the sedimentary environment from the Late Cretaceous to the Eocene can be traced, with a successive increase in the normalized alkalinity modulus and a decrease in the phemic modulus. On the paleogeodynamic discriminant diagrams (FeO*+MgO)–TiO2 and F1–F2, figurative points of Upper Cretaceous–Lower Eocene mudstones form petrochemical distribution trends that cover the fields of geodynamic conditions from passive to active margins of the sedimentation basin. The Cretaceous–Paleogene pelagic mudstones of the Sicilian domain of the Alpine Belt have a similar distribution of petrochemical components. The mineral assemblages found in the mudstones of the Skyba and Boryslav-Pokuttya nappes are typical for deep-sea clay sediments of modern oceans. The presence of montmorillonite and chlorite in the background argillites indicates a high probability of a contribution to the background petrofund of the sedimentatary basin by a femic magmatic component of endogenous origin, realized as a manifestation of volcanic and hydrothermal activity, synchronous to sedimentogenesis. An exogenous source could be the magmatic material transferred or changed by the processes of halmyrolysis, which is not synchronous with sedimentation. The Late Cretaceous–Early Eocene geodynamic events of the Outer Carpathian sedimentary basin developed in front of the ALCAPA and Tisza-Dacia terranes of the Alpine Tethys. Convergence, which caused the formation of the accretionary prism in front of the terranes, could contribute to the riftogenic (?) opening, deepening of the Outer Carpathian Basin, where the penetration of synsedimentary endogenous material, mainly of the basic composition, took place. This material could be the rock-forming source of the basin substrate, on which the mineral assemblages of the background hemipelagic mudstones were formed.