Background/Objectives: High in-domain accuracy of diabetic retinopathy (DR) grading models does not guarantee a similar performance for a new population. We quantify how an APTOS-trained grader degrades on an independent cohort and examine why. Metho...
Automated retinal vessel segmentation can support quantitative analysis of fundus photographs and facilitate measurement of vascular characteristics associated with ocular and systemic disease. Although deep-learning methods achieve high segmentation...
Background: Artificial intelligence (AI) has shown promise in breast ultrasound image analysis, but most evidence still comes from single-dataset studies. Clinical translation requires evaluation under heterogeneous acquisition and curation condition...
Breast cancer remains a major global health concern, making accurate lesion assessment essential for effective clinical decision making. Deep learning has shown promising performance in breast ultrasound analysis, yet models evaluated on data from th...
Background/Objectives: Artificial intelligence (AI) models for glaucoma screening using colour fundus photography have shown strong internal performance; however, their external validity and calibration reliability remain uncertain. This study develo...
Background/Objectives: Automated phonocardiogram (PCG) analysis is a candidate tool for scaling up cardiac auscultation, but the internal accuracy reported in many studies may not reflect performance when a model is applied to recordings from a diffe...
Background/Objectives: Artificial intelligence is increasingly applied to dental imaging, yet favorable internal performance does not necessarily indicate clinical transferability. This systematic review evaluated whether imaging-based dental artific...
With the large-scale deployment of Internet of Things (IoT) devices in smart homes, smart healthcare, and industrial internet scenarios, network attacks against IoT environments have become increasingly sophisticated, making reliable intrusion detect...
Breast cancer remains one of the most serious diseases threatening human health globally. To address diagnostic subjectivity and insufficient classification granularity in pathological screening, this study proposes a lightweight complementary convol...
Digital infrastructures are increasingly exposed to diverse and evolving cyber threats, highlighting the need for robust intrusion detection systems (IDSs). Although machine learning (ML)-based IDSs have achieved strong performance, most existing fra...
Epilepsy affects over 50 million people worldwide, yet automated seizure detection systems either achieve moderate sensitivity with excessive false alarms or rely on uninterpretable deep networks. This study presents a patient-independent EEG-based s...
Background: Diabetic cardiomyopathy (DCM) is characterized by metabolic dysfunction, inflammation, extracellular matrix (ECM) remodeling, and myocardial fibrosis. Increasing evidence suggests that ferroptosis-associated oxidative injury may contribut...
This study investigates automated fake news detection as a reliability-oriented text classification problem in dynamic digital information environments. We propose H-FuseNet, a hybrid multi-representation fusion framework that combines pretrained tra...
Cross-dataset nucleus instance segmentation requires robust separation of touching nuclei despite changes in tissue appearance. We introduce contact-aware multi-granular instance geometry prototypes (CA-MIGP), a training-time objective that assigns s...
Breast cancer screening is a fundamentally probabilistic diagnostic task that requires precise identification of complex imaging characteristics from diverse patient cohorts. Despite improvements in deep learning techniques, current automatic tools a...
Background/Objectives: To evaluate the effect of detector architecture and dataset characteristics on intracranial hemorrhage (ICH) subtype localization on noncontrast head CT, with emphasis on bidirectional cross-dataset generalization. Methods: Thi...
Pulmonary nodule malignancy classification requires effective integration of heterogeneous imaging features and contextual information among nodules. Existing models mainly analyze nodules independently and may overlook inter-nodule relationships. We...
Background: Rare neurological diseases are challenging to diagnose from brain MRI because of their low prevalence, heterogeneous imaging patterns, and limited annotated datasets. Deep learning may support image-level recognition, but results from cur...
Phishing sites are increasingly causing harm to consumers, commercial enterprises, and the online infrastructure. Online safety is dependent on how well these evil sites can be detected in time and correctly. A number of solutions that exist are base...
Background/Objectives: This study proposes CMT-BUSNet, a hybrid architecture integrating CNN, Mamba, and Transformer branches for breast ultrasound tumor segmentation with built-in explainability. Methods: CMT-BUSNet employs a CNN-anchored hierarchic...
Gut microbiota dysbiosis has been associated with ischemic stroke (IS), myocardial infarction (MI), and aging, but whether these contexts share reproducible microbial features remains unclear. We conducted an exploratory and hypothesis-generating des...
Melanoma accounts for a small fraction of skin cancer cases but for the majority of skin-cancer-related deaths, making accurate early detection essential. Although deep learning-based dermoscopic image analysis has shown strong diagnostic potential,...
Nuclei instance segmentation in hematoxylin and eosin (H&E)-stained images plays an important role in automated histological image analysis, with various applications in downstream tasks. While several machine learning and deep learning approache...
Background/Objectives: The precise classification of non-small-cell lung cancer (NSCLC) into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) has important role in treatment decisions and in prognosis. Proper subtyping ensures that...
Background: Artificial intelligence (AI)-driven gait analysis using plantar pressure and ground reaction force (GRF) signals may provide objective digital biomarkers for rehabilitation, but clinical translation remains uncertain. This scoping review...
Background: Magnetic resonance imaging (MRI) is a primary modality for brain tumor diagnosis and treatment planning, making accurate automated classification critically important. However, existing deep learning approaches face limitations in predict...
Cross-dataset pothole segmentation is difficult because illumination, camera characteristics, asphalt texture, and boundary quality vary between training and deployment environments. This study proposes PotholeBAF-Net, an EfficientNet-B3 encoder&ndas...
This study proposes TPA-ConvNeXt, a ConvNeXt-Small-based deep learning architecture for retinal image classification using a phase-based trigonometric attention mechanism. The proposed Trigonometric Phase Attention (TPA) block recalibrates feature ma...
Background: Deep learning approaches, especially convolutional neural networks (CNNs) and Vision Transformers (ViTs), have made substantial progress in breast cancer classification from histopathological images. Yet, previous CNN–ViT-based arch...
Objectives: Accurate severity classification is important for sensor-based assessment of Parkinson’s disease, but overlap between adjacent stages and class imbalance can reduce model robustness. This study aimed to develop and evaluate a dual-t...
Accurate prediction of ultra-high-performance concrete (UHPC) compressive strength is essential for optimizing mixture design and reducing experimental iterations. Existing machine learning approaches suffer from limited algorithm diversity, insuffic...
Background/Objectives: Hippocampus segmentation in brain MRI is challenging because the structure is small, irregularly shaped, and poorly contrasted against surrounding tissue. Increasingly complex U-Net variants improve accuracy at computational co...
Background: Explainable artificial intelligence (XAI) is increasingly proposed to improve trust in mammography-based artificial-intelligence systems, but the validity and clinical readiness of published explanations remain unclear. We aim to systemat...
Background/Objectives: Ultrasound is widely used for breast cancer screening and diagnosis, particularly in low- and middle-income settings, but its diagnostic reliability is often compromised by speckle noise that degrades lesion margins and tissue...
Stress-associated transcriptional programs are common in single-cell perturbation data, but they are often treated as technical or experimental nuisance signals. Whether rare high-stress populations arise stochastically after perturbation or reflect...
The progression of tuberculosis (TB) from a latent infection to an active disease involves intricate modifications in host immune, inflammatory, oxidative, and metabolic pathways. Ferroptosis represents a distinct form of regulated cell death that re...
Weed competition represents a primary constraint in sesame production, causing substantial yield losses typically ranging from 18 to 68% under inadequate control measures. Precise crop–weed discrimination remains challenging due to morphologica...
Background/Objectives: Early and accurate detection of breast cancer is essential for reducing mortality and improving patient outcomes. However, the manual interpretation of breast ultrasound images is challenging due to image variability, noise, an...
Background: Accurate white blood cell (WBC) segmentation is important for quantitative microscopic image analysis, but conventional supervised approaches depend on labor-intensive pixel-level annotations and may exhibit reduced robustness across data...
Background: Accurate segmentation of white blood cells (WBCs) in peripheral blood smear images is a fundamental step in computational hematology, enabling downstream tasks such as classification, morphological assessment, and quantitative analysis. H...
Real-time monitoring of crop responses to environmental deviations represents a new avenue for applications of remote and proximal sensing. Combining the high-throughput devices with novel machine learning (ML) approaches shows promise in the monitor...
Occupational mental-health risk in knowledge-work settings is an important public-health and psychosocial-support concern because workload demands, career insecurity, limited mentoring, uneven institutional support and barriers to care can increase p...
Monocular 3D human pose estimation is sensitive to depth ambiguity and upstream 2D-detector error. We investigate a detector-conditioned virtual 2D pose as an auxiliary hypothesis rather than an independent physical measurement. Joint reliability com...
Building facades are continually exposed to weathering, material aging, hygrothermal cycling, and service-related disturbances. Cracks, delamination, spalling, and seepage can compromise durability and safety, while associated thermal anomalies provi...
Breast cancer remains the leading cause of cancer-related deaths among women globally. Early detection through mammography is vital for improving survival rates; however, the large volume of medical images and subtle variations in lesion characterist...
Artificial intelligence (AI) has shown strong potential in dermoscopic image classification; however, reliable clinical deployment remains challenging due to limited generalizability across populations and institutions. This challenge is further comp...
Single-cell RNA sequencing (scRNA-seq) studies increasingly combine expression-based and mutation-derived signals, but small-cohort designs with repeated runs from the same biological unit can make standard cross-validation overly optimistic. We rean...
Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the assessment of various spinal pathologies, including degenerative disc disease, spinal stenosis, and spondylolisthesis. The accurate identification and quantification...
Public concrete datasets often contain duplicate records, coupled variables, and cross-source distribution shifts, which may lead to overly optimistic model evaluation. Based on a deduplicated UCI high-performance concrete dataset (1005 samples), thi...
Background/Objectives: Osteoporosis is a major public health concern associated with increased fracture risk and reduced quality of life if not detected at an early stage. Automated analysis of knee X-ray images using artificial intelligence has show...
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