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        <title>Frontiers in Computer Science | Computer Vision section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/computer-science/sections/computer-vision</link>
        <description>RSS Feed for Computer Vision section in the Frontiers in Computer Science journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-07-28T23:02:30.988+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1819991</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1819991</link>
        <title><![CDATA[A unified benchmarking framework for vector databases in scalable embedding-based image retrieval systems]]></title>
        <pubdate>2026-07-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Somula Ramasubbareddy</author><author>S. Remya</author><author>Sathishkumar Veerappampalayam Easwaramoorthy</author><author>Gaddam A. Sanjeeva Reddy</author><author>Yong Yun Cho</author><author>Manu J. Pillai</author>
        <description><![CDATA[Modern image retrieval and similarity search systems rely on high-dimensional embeddings that are produced by deep neural networks. To store and query these embeddings, vector databases are used. However, the lack of standardized, workload-aware benchmarks makes selecting the right system for a given workload challenging. This paper presents an adaptive benchmarking framework that systematically evaluates vector databases using realistic image-embedding workloads. The framework integrates CLIP ViT-B/32 (512-dimensional) and ResNet-50 embedding models with a consistent indexing and querying pipeline, thereby enabling fair and reproducible evaluation across the entire data lifecycle. We benchmarked four leading vector database systems–FAISS, Milvus, Qdrant, and pgvector–using CIFAR-10 and STL-10 image embeddings scaled from 100 to 10,000 vectors, with cross-dataset validation confirming that relative system rankings are consistent across different embedding distributions, with additional scalability tests up to 5 × 104 vectors. Our experiments reveal distinct performance trade-offs: FAISS inserts 10,000 vectors in 15 ms and answers queries in under one millisecond, but lacks built-in persistence. In contrast, pgvector, because of the transactional overhead typical of relational systems, needs 14.9 s for the same insertion task. Milvus balances 47–56 ms query latency with perfect Recall@5 across the lifecycle scales (100–10,000 vectors), with a small drop to 0.98–0.99 at the extended scalability scale of 50,000 vectors due to IVF cluster-boundary quantization error, while Qdrant, which offers the quickest update performance among persistent systems (approximately 2.7 ms across all tested scales), compromises on recall variability (0.88–1.00). At all tested scales, each of FAISS, Milvus, and pgvector scored perfect Recall@5. To support deployment decisions, a workload-aware multi-objective scoring model is suggested that considers latency, memory, and accuracy according to application requirements. The framework thus provides practical deployment insights for practitioners and also provides a reproducible foundation for vector database evaluation in AI-powered image retrieval.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1798168</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1798168</link>
        <title><![CDATA[Learning under extreme data scarcity: an enhanced hybrid CRNN with calibration and test-time augmentation for multi-class lung CT classification]]></title>
        <pubdate>2026-07-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>V. Maheswari</author><author>H. Parveen Sultana</author>
        <description><![CDATA[IntroductionThis study evaluates multi-class lung CT classification under extreme data scarcity.MethodsFrom a 400/72/295 train/validation/test split, only a class-balanced 5% coreset (20 images) is used for training. Six CNN baselines (ResNet-18, ResNet-50, DenseNet-121, MobileNetV3-Large, EfficientNet-B0, ConvNeXt-Tiny) are compared with a hybrid CRNN (EfficientNet-B0 encoder, bidirectional LSTMs, multi-head attention). Experiments are repeated over 3 seeds x 3 coreset samplings (nine runs per model), reported as mean +/- SD and validated with paired Wilcoxon signed-rank tests (Bonferroni correction); test-time augmentation (TTA) and post-hoc temperature scaling are assessed.ResultsThe proposed model attains accuracy 0.4523 +/- 0.0085, macro-F1 0.4637 +/- 0.0085, and macro-AUC 0.7302 +/- 0.0073, outperforming the best baseline ResNet-18 (0.3842 +/- 0.0089; p < 0.01, Cohen’s d = 7.89). TTA preserves accuracy (0.4557 +/- 0.0085) and gives the highest single-model macro-AUC (0.7357 +/- 0.0069); a top-3 ensemble reaches macro-AUC 0.7428 +/- 0.0072 but lowers accuracy (0.4105 +/- 0.0075). An ablation study attributes the largest gain to BiLSTM (+5.76%), with attention (+3.72%) and full combination (+10.00%). Farthest-point coreset selection yields higher mean accuracy and lower variance.DiscussionTemperature scaling is model-dependent; cross-seed analysis gives a stable T = 1.257 +/- 0.063, reducing ECE from 0.1557 +/- 0.0050 to 0.1190 +/- 0.0037, revising the original single-run finding.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1851511</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1851511</link>
        <title><![CDATA[DSF-Net: dual selective fusion network with spatial-frequency domain encoding for retinal vessel segmentation]]></title>
        <pubdate>2026-07-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Feng Liang</author><author>Xiaoqi Sheng</author><author>Yang Liu</author><author>Ruizhuo Li</author><author>Yanmin Shi</author>
        <description><![CDATA[IntroductionRetinal vessel segmentation is a fundamental task in quantitative fundus image analysis. However, existing methods still face challenges in segmenting thin and complex vessels because local details and global contextual information are often insufficiently integrated.MethodsTo address this issue, we propose a Dual Selective Fusion Network (DSF-Net) for retinal vessel segmentation. The proposed network consists of a Dual-Branch Encoder (DB-Encoder), a Pinwheel-based Local Attention (PLA) module, and a Dual Selective Fusion Transformer Block (DSFTB). The DB-Encoder jointly models spatial- and frequency-domain information to capture both fine vessel details and global contextual patterns. The PLA module enhances local perception and boundary sensitivity through asymmetric multidirectional convolutions and Sobel edge priors. The DSFTB integrates Multi-scale Feature Attention (MSFA) and token-selective Global Feature Attention (GFA) to enable adaptive feature fusion and long-range dependency modeling.ResultsExperiments conducted on the DRIVE, STARE, and CHASE_DB1 datasets demonstrate that DSF-Net achieves competitive overall performance compared with existing methods. In particular, the proposed method produces more accurate and structurally coherent segmentation results, especially for thin and complex vessels.DiscussionThese findings indicate that the combined modeling of local detail, frequency-aware representation, and global contextual dependency is effective for retinal vessel segmentation. DSF-Net provides a robust framework for improving vessel continuity and boundary delineation in fundus images. The source code is available at: https://github.com/liang050629/DSF-Net.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1809919</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1809919</link>
        <title><![CDATA[DMFNet: exploring diverse mid-feature for visible-infrared person re-identification]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ganqing Mo</author><author>Yanbing Chen</author><author>Hairong Ye</author>
        <description><![CDATA[IntroductionVisible-infrared person re-identification remains a challenging task due to inherent modality discrepancies between RGB and infrared images. Existing methods often struggle to effectively capture both modality-specific and modality-invariant features simultaneously, limiting their cross-modal matching performance.MethodsThis paper presents DMFNet (Diverse Mid-feature Network), a novel deep learning architecture that effectively harnesses intermediate shared features to bridge this cross-modal gap. DMFNet integrates two key modules: a Multi-layer Feature Cascade Module (MFCM) that aggregates discriminative features across different network stages, and a Dual Feature Generation Module (DFGM) that produces diverse intermediate representations through Instance-Batch Normalization variants.ResultsExtensive experiments on the SYSU-MM01 and RegDB datasets demonstrate that DMFNet achieves state-of-the-art performance, with significant improvements in Rank-1 accuracy (up to 8.2% on SYSU-MM01 and 6.5% on RegDB) and mean Average Precision (mAP) over existing methods.DiscussionOur approach not only enhances cross-modal matching capabilities but also provides interpretable feature visualizations, offering valuable insights into the network's decision-making process. These results pave the way for more robust person re-identification systems in real-world surveillance scenarios, particularly in low-light conditions where traditional visible-only systems often fail.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1837091</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1837091</link>
        <title><![CDATA[LightFormer-3D: a lightweight hierarchical CNN-transformer hybrid for 3D medical image segmentation]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qingzheng Hu</author><author>Ji Li</author><author>Wenqiu Zhu</author>
        <description><![CDATA[IntroductionBrain tumor segmentation from 3D MRI is critical for treatment planning, yet deploying accurate Transformer-based models in clinical settings remains challenging due to prohibitive computational costs. State-of-the-art methods often exceed 90M parameters and require seconds per inference, precluding real-time use on resource-constrained hardware.MethodsWe propose LightFormer-3D, a lightweight hierarchical CNN-Transformer hybrid that applies a co-design principle to jointly optimize patch embedding, self-attention, and multi-scale feature fusion, ensuring that efficiency gains compound across all stages. Specifically, a Depthwise Separable Convolution Patch Embedding (DSC-PE) reduces embedding parameters by 70%; a Multi-head Self-Attention with Spatial Sequence Reduction (MSA-SR) compresses self-attention complexity from O(N2) to O(N2/r3); and a Lightweight Scalable Feature Fusion (LSFF) module adaptively weights multi-scale features using only five parameters.ResultsEvaluated on the BraTS 2017 and BraTS 2021 benchmarks, LightFormer-3D achieves a mean Dice score of 87.0% on BraTS 2021 with 1.78M parameters and 9.3 GFLOPs, surpassing UNETR (80.2% Dice, 92.5M parameters) by 6.8 pp and the best competing lightweight method, U-Net_ASPP_EVO (86.0%), by 1.0 pp, while delivering 10× faster inference than UNETR (0.51s vs. 5.41s per case on an NVIDIA RTX 4060). Five-fold cross-validation (86.7% ± 0.2%) confirms stable generalization. Ablation studies validate each module's contribution, and zero-shot external validation on the independent MU-Glioma-Post dataset (203 post-treatment cases) confirms robust whole-tumor localization (WT Dice 85.0%) under severe domain shift.DiscussionLightFormer-3D establishes that co-designed lightweight modules can simultaneously improve accuracy and efficiency, achieving a new Pareto-optimal point for brain tumor segmentation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1707808</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1707808</link>
        <title><![CDATA[MoDiPO: text-to-motion alignment via AI-feedback-driven direct preference optimization]]></title>
        <pubdate>2026-07-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Massimiliano Pappa</author><author>Luca Collorone</author><author>Giovanni Ficarra</author><author>Indro Spinelli</author><author>Fabio Galasso</author>
        <description><![CDATA[Diffusion Models have revolutionized the field of human motion generation by offering exceptional generation quality and fine-grained controllability through natural language conditioning. Their inherent stochasticity, that is the ability to generate various outputs from the same input prompt, is key to their success. However, this diversity should not be unrestricted, as it may lead to unlikely generations. Instead, it should be confined within the boundaries of text-aligned and realistic generations. To address this issue, we propose MoDiPO (Motion Diffusion DPO), the first methodology to adapt Diffusion Direct Preference Optimization to align text-to-motion diffusion models. We streamline the laborious and expensive process of gathering human preferences needed in DPO by leveraging AI feedback instead. This enables us to experiment with novel DPO strategies, using both online and offline generated motion-preference pairs. To foster future research we contribute with a motion-preference dataset which we dub Pick-a-Move. We demonstrate, both qualitatively and quantitatively, that our proposed method yields significantly more realistic motions. In particular, MoDiPO achieves statistically significant improvements in Fréchet Inception Distance (FID) of up to 39% on MLD/HumanML3D and consistent gains of 9%–15% across both MLD and MDM on HumanML3D and KIT-ML. Finally, MoDiPO secures a threefold increase in preference from human evaluators compared to the original models' outputs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1830781</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1830781</link>
        <title><![CDATA[Shot boundary detection for locating subliminal stimuli in films]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Javier Sanz-Aznar</author><author>Juan José Caballero-Molina</author><author>Carlos Aguilar-Paredes</author>
        <description><![CDATA[IntroductionWith the objective of detecting film shots with a duration that would qualify them as subliminal, this paper proposes a shot boundary detection system to locate shots in a video that last for only one or two frames.MethodsThis design prioritizes the complete retrieval of extremely short shots, minimizing false negatives, even at the expense of additional false positives. This is achieved by a sequential filtering strategy, integrating complementary metrics in a cascade, ensuring robustness against motion, luminance variations and noise while reducing the processing time by progressively ruling out potential cases for comparison. The series of processes adopted were a color histogram analysis with a dynamic threshold, a mean squared error analysis (MSE), a structural similarity index measure (SSIM) and a histogram correlation analysis. The system design was tested on 10 films in which the shots that had to be detected were known beforehand, and subsequently on 15 other films that may potentially contain subliminal shots.ResultsThe results reveal the effective detection of 100% of the shots, although the erroneous detections produced require a visual review at the end of the process. Finally, the analysis was applied to 1,247 films to verify, in a real-world scenario, whether the designed system is effective in detecting one- or two-frame shots.DiscussionIt was thus determined that the design proposed can facilitate the identification of short shots for a large number of films, meeting the initial objective of the design and confirming the usefulness of this system for detecting subliminal stimuli in films.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1841416</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1841416</link>
        <title><![CDATA[Attention-guided hybrid CNN–transformer framework for cross-modality COVID-19 detection and lesion localization in CT and chest X-ray images]]></title>
        <pubdate>2026-06-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Manas Gautam</author><author>P. Jagalingam</author>
        <description><![CDATA[Accurate and interpretable analysis of thoracic medical images is essential for reliable COVID-19 diagnosis; however, existing approaches face significant challenges in jointly performing classification and lesion localization across multiple imaging modalities. Suboptimal diagnostic knowledge and limited spatial awareness are the results of conventional deep learning approaches, especially models based on convolutional neural networks (CNNs). These methods mainly concentrate on local feature extraction and often handle classification and segmentation as independent tasks. Although transformer-based models enhance global contextual learning, their practical use in real-world clinical settings is limited due to their high computing resource requirements and large-scale dataset requirements. On top of that, there are currently no explainability procedures built into hybrid CNN-Transformer methods, and most of them use shallow or one-directional fusion algorithms. In order to overcome these shortcomings, this research suggests a CNN-Transformer hybrid attention-guided paradigm for COVID-19 identification and lesion localization across modalities utilizing separate CT and CXR datasets. The suggested design incorporates a ResNet-18 backbone for local feature extraction, a light Vision Transformer for global contextual modelling, a bidirectional cross attention fusion module for effective interaction between CNN and transformer representations, a feature refinement mechanism based on convolutional block attention module (CBAM), and a unified multi-task learning framework. In the CT data set, the experimental results prove that the proposed model is superior to the other models, having an accuracy of 98.29%, a precision of 98.14%, a recall of 98.13%, and an F1-Score of 98.11%. The model also has a Dice score of 0.847 and an Intersection-over-Union score of 0.848, respectively. The proposed model gets an accuracy of 97.74%, a precision of 97.93%, a recall of 97.52%, and an F1-Score. By enhancing generalizability and clinical reliability across imaging modalities, our findings demonstrate that the suggested paradigm delivers accurate, robust, and interpretable COVID-19 diagnosis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1753080</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1753080</link>
        <title><![CDATA[Hardware- and software-based fingerprint liveness detection schemes—A literature review]]></title>
        <pubdate>2026-06-02T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Rubab Mehboob</author><author>Hassan Dawood</author><author>Ali Daud</author><author>Hussain Dawood</author>
        <description><![CDATA[Technological advancements have shaped the methods for ensuring personal safety and safeguarding digital assets. In this regard, biometric systems are innovative technological solutions that utilize various behavioral and physiological traits for personal verification and identification. This technology provides efficient and seamless authentication while improving users' convenience in a range of security applications, including border security, physical access control, healthcare, and financial transactions. Fingerprint-based verification systems are vulnerable to various attacks, including spoofing, presentation attacks, and latent fingerprint attacks. Henceforth, it is essential to incorporate a robust and reliable liveness detection mechanism into the authentication system to enhance the security of such systems. Since fingerprint liveness detection has been an active area of research, many techniques have been proposed for handling this problem by extracting different characteristics such as minutiae, ridges, texture, pores, perspiration, and morphological features. In this article, the state-of-the-art liveness detection schemes along with their taxonomy from 2003 to 2025 are chronologically analyzed. In this article, we have presented the techniques, their parameters, their pros and cons, and the evaluation matrices. Moreover, this article inculcates a comprehensive survey of the various hardware-, software-based approaches focusing on the deep learning liveness detection techniques. We have also investigated the standard benchmarks of the LivDet series that are publicly available and the evaluation protocols.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1789702</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1789702</link>
        <title><![CDATA[Comparative analysis of previous YOLO detectors and YOLOv26s for real-time weapon detection in video surveillance]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Carlos Julio Fierro Silva</author><author>Carolina Del-Valle-Soto</author><author>Carlos Bran</author><author>José Varela-Aldás</author>
        <description><![CDATA[Real-time weapon detection in video surveillance is a critical capability for artificial intelligence assisted security systems, particularly in scenarios constrained by low latency, limited computational resources, and strict power efficiency requirements typical of edge artificial intelligence deployments. This work presents a comparative analysis of lightweight YOLO based object detectors, namely YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s, vs. the recently introduced YOLOv26s model. In contrast to conventional benchmarking studies, this work extends the evaluation to real-world edge deployment conditions using an NVIDIA Jetson Nano device, explicitly measuring end-to-end latency, including preprocessing, inference, and post-processing stages. While earlier YOLO variants primarily relied on convolutional neural network architectures and intermediate explorations such as attention centered designs aimed to improve detection accuracy, YOLOv26 represents a paradigm shift by being designed from the ground up for low power edge devices, emphasizing architectural simplicity and deployment efficiency. To ensure a fair and reproducible evaluation, all models are trained on the same weapon detection dataset under a unified experimental protocol using small scale variants.The experimental results reveal that, despite exhibiting comparable inference times, different models show significantly different real-time performance due to variations in post-processing complexity. Specifically, models such as YOLOv8s, YOLOv9s, and YOLOv11s incur a substantial post-processing overhead, whereas YOLOv10s and YOLOv26s produce compact output representations that drastically reduce post-processing cost.This leads to a clear separation in deployment behavior, where end-to-end latency is reduced from approximately 300 ms to 125–130 ms, effectively doubling the achievable frame rate on embedded hardware. Rather than proposing a universal ranking, the study analyzes the trade offs introduced by architectural evolution and optimization strategies, providing technical criteria to support model selection under resource constrained deployment scenarios and demonstrating that post-processing efficiency, rather than inference speed alone, is the dominant factor in real-time edge performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1824259</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1824259</link>
        <title><![CDATA[Correction: An improved contrastive learning loss function for automated clock-drawing test grading with implications for cognitive impairment screening]]></title>
        <pubdate>2026-03-31T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Ning Liu</author><author>Qian Sun</author><author>Xiaoyin Xu</author><author>Haifeng Mou</author><author>Xinhai Liao</author><author>Bokai Rong</author><author>Lingxing Wang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1753764</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1753764</link>
        <title><![CDATA[Single-item training for multi-dish recognition: a class-agnostic framework for Indian food platters]]></title>
        <pubdate>2026-03-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Keerthi Garisa</author><author>Ravi Kant Kumar</author><author>Priyanka Singh</author>
        <description><![CDATA[Accurate dietary assessment is increasingly dependent on automated food recognition systems capable of operating effectively in real-world environments. While most vision-based models perform well on single-item datasets, their performance degrades significantly in complex multi-dish settings. This scenario is particularly evident in Indian thalis, which contain overlapping food items with diverse textures and high visual variability. These challenges make large-scale multi-dish annotation expensive and limit practical deployment of such systems. To address this gap, we propose a novel two-stage framework that enables recognition of multi-dish food images using only single-item training data. The proposed pipeline incorporates class-agnostic segmentation using the Segment Anything Model (SAM), followed by classification with an SE-DenseNet121 network optimized via Optuna-based hyperparameter tuning.The model is trained exclusively on single-item annotated images and generalizes to multi-item thali images at inference time through a segmentation-classification mapping strategy. This zero-shot segmentation approach eliminates the need for multi-dish ground-truth annotations. As a result, the annotation complexity is reduced from O(N × M) to O(N). The proposed system achieves accuracy of 97.48% on single-item food image classification and demonstrates strong applicability to multi-dish Indian thali images through region-wise inference on segmented food items. Furthermore, the framework is computationally efficient, achieving 2 × faster inference with a latency of 1.58 ms while using only 70% of the parameters required by transformer-based baselines. It operates with low computational cost (2.90 GFLOPs), significantly fewer parameters (8.06M compared to 26.69–86.77M), and delivers higher throughput (633.32 samples/s). These results demonstrate that the proposed method provides a scalable and practical solution for real-time dietary assessment applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1690044</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1690044</link>
        <title><![CDATA[An improved contrastive learning loss function for automated clock-drawing test grading with implications for cognitive impairment screening]]></title>
        <pubdate>2026-02-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ning Liu</author><author>Qian Sun</author><author>Xiaoyin Xu</author><author>Haifeng Mou</author><author>Xinhai Liao</author><author>Bokai Rong</author><author>Lingxing Wang</author>
        <description><![CDATA[Contrastive learning has been attracting much interest in recent years for its ability to train without labeled data. An important factor in its success is the loss function, which guides the search for prominent features that separate the positive and negative classes. The triplet loss function is widely used in contrastive learning, in which the objective is to attract a pair of positive instances while pushing away a negative instance from the anchor instance, where one of the positive instances is often an augmented version of the anchor. To improve the performance of contrastive learning in automated Clock-Drawing Test (CDT) grading, this paper proposes a more comprehensive triplet loss function that aims to not only keep the distance between the anchor and a positive instance small and the distance between the anchor and a negative instance large, but also keep the distance between the positive and negative instances large. Experimental results show that the improved loss function significantly improves the model’s accuracy, precision, recall, and F1-score by 3–5% on both CIFAR-10 and CDT datasets, providing a new method for improving the accuracy of automatic CDT scoring and early detection of cognitive impairments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1763780</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1763780</link>
        <title><![CDATA[PrecisionMicro-DETR: enhancing small pulmonary nodule detection in CT scans with multi-scale feature fusion and lightweight design]]></title>
        <pubdate>2026-02-11T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Jianle Chen</author><author>Jianyu Zhu</author><author>YuYan Lin</author><author>Fuqin Deng</author><author>Lanhui Fu</author><author>Huilian Liao</author>
        <description><![CDATA[To address the common issue of insufficient accuracy in existing detection models when dealing with morphologically complex and minute pulmonary nodules, this study proposes an enhanced detection model called PrecisionMicro-DETR based on the RT-DETR architecture. The model introduces a feature enhancement fusion module tailored for small targets in the detection head to strengthen the feature extraction capability for subtle structures (Strengthen the integration of small target features, SSTF). It also incorporates a Modulation Fusion Module (MFM) to effectively improve discriminative performance in areas with blurred boundaries between lesions and normal tissues. Additionally, a lightweight neck network based on SNI-GSConvE is introduced to optimize computational load while maintaining high accuracy. Experimental evaluation shows that PrecisionMicro-DETR achieves a mean average precision (mAP) of 94.9% on the publicly available Tianchi dataset. Its robustness and generalization ability in real diagnostic environments are further validated through clinical CT images from hospital PACS systems. This study provides a high-precision and efficient solution for CT pulmonary nodule detection, contributing positively to advancing the clinical application of intelligent assisted diagnostic systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1721892</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1721892</link>
        <title><![CDATA[Adaptive self-attention for enhanced segmentation of adult gliomas in multi-modal MRI]]></title>
        <pubdate>2026-02-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Evan P. Savaria</author><author>Jiangwen Sun</author>
        <description><![CDATA[Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We evaluated AIMS on the BraTS 2019 adult glioma dataset using five-fold cross-validation and compared it against strong hybrid baselines with paired statistical testing; generalization was assessed on an independent BraTS 2021 cohort without fine-tuning. AIMS achieved high ensemble Dice Similarity Coefficients of 0.936 for enhancing tumor, 0.942 for tumor core, and 0.931 for whole tumor on BraTS 2019, with statistically significant improvements over competing methods, and maintained strong performance on BraTS 2021 despite protocol and scanner variability. Finally, Grad-CAM-based explanations applied to adaptive attention and fusion layers, together with quantitative sanity checks, provided modality-aware and spatially meaningful visualizations that support clinical interpretation. By improving both segmentation accuracy and model transparency relative to strong baselines, AIMS advances multi-modal glioma segmentation and helps bridge human–machine teaming by enabling faster, clinician-aligned tumor delineation without sacrificing reliability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1639421</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1639421</link>
        <title><![CDATA[Using domain adaptation and transfer learning techniques to enhance performance across multiple datasets in COVID-19 detection]]></title>
        <pubdate>2026-01-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Vengai Musanga</author><author>Colin Chibaya</author><author>Serestina Viriri</author>
        <description><![CDATA[This study presents a hybrid neuro-symbolic framework for COVID-19 detection in chest CT that combines multiple deep learning architectures with rule-based reasoning and domain-adversarial adaptation. By aligning features across four heterogeneous public datasets, the system maintains high, site-independent performance (average accuracy = 97.7%, AUC-ROC = 0.996) without retraining. Symbolic rules and Grad-CAM visualizations provide clinician-level interpretability, achieving near-perfect agreement with board-certified radiologists (κ = 0.89). Real-time inference (23.4 FPS) and low cloud latency (1.7 s) meet hospital PACS throughput requirements. Additionally, the framework predicts key treatment outcomes, such as intensive care unit (ICU) admission risk and steroid responsiveness, using retrospective EHR data. Together, these results demonstrate a scalable, explainable solution that addresses cross-institutional generalization and clinical acceptance challenges in AI-driven COVID-19 diagnosis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1692523</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1692523</link>
        <title><![CDATA[DeepGeoFusion: personalized facial beauty prediction through geometric-visual fusion]]></title>
        <pubdate>2026-01-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kunwei Wang</author><author>Yanzhi Li</author><author>Dong Huang</author><author>Junmei Feng</author><author>Xiaoyi Feng</author>
        <description><![CDATA[IntroductionPersonalized facial beauty prediction is a critical advancement beyond population-level models with transformative applications in aesthetic surgery planning and user-centric recommendation systems, while contemporary methods face limitations in modeling aesthetically sensitive facial regions, fusing heterogeneous geometric and visual features, and reducing extensive annotation dependency for personalization.MethodsWe propose DeepGeoFusion, a novel framework that synergizes Vision Mamba-extracted global visual features with anatomically constrained facial graphs (constructed from 86 landmarks via Delaunay triangulation), using the Graph Node Attention Projection Fusion (GNAPF) block for cross-modal alignment and a lightweight adaptation mechanism to generate personalized preference vectors from 10 seed images via confidence-gated optimization.ResultsExtensive experiments on SCUT-FBP5500 demonstrate statistically significant improvements in personalized prediction accuracy and robust performance across genders and ethnicities compared to state-of-the-art methods.DiscussionDeepGeoFusion effectively addresses key limitations of existing methods by integrating complementary geometric and visual features, enabling efficient personalization with minimal annotation and highlighting practical value for aesthetic-related applications requiring personalized assessments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1714394</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1714394</link>
        <title><![CDATA[Toward real-time emotion recognition in fog computing-based systems: leveraging interpretable PCA_CNN, YOLO with self-attention mechanism]]></title>
        <pubdate>2026-01-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nora EL Rashidy</author><author>Eman Allogmani</author><author>Esraa Hassan</author><author>Khaled Alnowaiser</author><author>Hela Elmannai</author><author>Zainab H. Ali</author>
        <description><![CDATA[Emotion estimation from face expression analysis has been extensively examined in computer science. In contrast, classifying expressions depends on appropriate facial features and their dynamics. Despite the promising accuracy results in handled and favorable conditions, processing faces acquired at a distance, entailing low-quality images, still needs an influential performance reduction. The primary objective of this study is to introduce a Real-Time Emotion Recognition system-based Fog Technique, which was developed to track and observe human emotional states in real time. This paper provides a comprehensive integration of PCA-based feature selection with a specific version of YOLO (YOLOv8), in addition to spatial attention for real-time recognition. The developed system demonstrates superiority in edge deployment capabilities compared to existing approaches. The proposed model is compared with the CNN_PCA hybrid model. First, Principal Component Analysis (PCA) is employed as a dimension-reduction tool, focusing on the most informative characteristics during training, and then CNN as classification layer. The proposed system's performance is assessed via a dataset of 35,888 facial photos classified into seven classes: anger, fear, happiness, neutral, sadness, surprise, and disgust. The constructed model surpasses established pre-trained models, such as VGG, ResNet, and MobileNet, with different evaluation metrics. First, the PCA_CNN model achieved superior accuracy, precision, recall, and Area Under the Curve (AUC) scores of 0.936, 0.971, 0.843, 0.871, and 0.943.YOLO v8 aith attention model achieved 0.986, 0.902, 0.941, and 0.952. Additionally, the model exhibits significantly faster processing time, completing computations in just 610 seconds than other pre-trained models. To validate the model's superiority, extensive testing on additional datasets consistently yields promising performance results, further validating the efficiency and effectiveness of our developed model in real-time emotion recognition for advancing affective computing applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1700167</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1700167</link>
        <title><![CDATA[EAC-YOLO: a surface damage identification method of lightweight membrane structure based on improved YOLO11]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zihang Yin</author><author>Limei Zhang</author><author>Huarong Liu</author><author>Qiuyue Du</author><author>Chongchong Yu</author>
        <description><![CDATA[Different surface damage can cause harm to membrane structures, and traditional manual inspection methods are inefficient and prone to missed detections and false alarms. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. To achieve automatic identification of typical surface damage in membrane structures, we construct a dataset comprising five damage types based on common types of surface damage in membrane structures and propose a lightweight identification algorithm for membrane structure surface damage, specifically EAC-YOLO. Firstly, the SPPF module is reconstructed, and the ECA lightweight attention mechanism is introduced to enhance the model’s ability to distinguish easily confused features. Secondly, ADown is introduced to replace the original down-sampling method, improving the retention ability of multi-scale damage features. Finally, the CGBlock and C3k2 modules are combined and reconstructed in the neck network to reduce the interference of damage background factors and capture more features of the damage and its surrounding environment. Experimental evaluation results on the established dataset show that the improved mAP50 value reaches 87.5%, and the number of parameters, computational cost, and model size are reduced by approximately 28, 25, and 28%, respectively, compared with the original model, demonstrating the advantages of a small size and high accuracy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1744581</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1744581</link>
        <title><![CDATA[Editorial: Foundation models for healthcare: innovations in generative AI, computer vision, language models, and multimodal systems]]></title>
        <pubdate>2025-11-26T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Sokratis Makrogiannis</author>
        <description></description>
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