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Computer Methods and Programs in Biomedicine Update
Scopus
ISSN 2666-9900 · Elsevier · 246 artikel di indeks
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Artikel Jurnal
A unified deep-learning-heuristic framework for MRI-based classification of Alzheimer’s, epilepsy, and aneurysms
MRI-based diagnosis of neurological disorders typically requires processing entire image volumes despite substantial inter-slice redundancy, resulting in unnecessary computational overhead. We present a computational framework that automates frame selection for three brain disorders, namely…
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Artikel Jurnal
A deep learning framework for skin lesion classification using ResNet50 and ELM
The classification of skin lesions is crucial for early diagnosis and effective treatment, particularly in reducing the mortality associated with melanoma, the most aggressive form of skin cancer. In this work, we propose a hybrid framework that combines a ResNet50 model trained from scratch with…
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Automated detection of Cutaneous Leishmaniasis in Giemsa-stained smears using a stain-aware U-Net: A diagnostic support tool for low-resource settings
Cutaneous leishmaniasis (CL) remains a major public health concern in endemic regions, where diagnosis commonly relies on microscopic examination of Giemsa-stained skin smears. This process is time-consuming, highly operator-dependent, and challenged by low parasite burden and staining variability…
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Hybrid-fingerprint machine learning framework for predicting inhibitors relevant to breast cancer
Breast cancer remains a leading cause of cancer morbidity and mortality, motivating rapid discovery of target-focused inhibitors and chemical probes that modulate hormone biosynthesis and metastatic progression. Among clinically relevant molecular targets are aromatase (CYP19A1) and matrix…
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Deep transfer learning for breast cancer classification in mammography: A systematic review
Background: Deep transfer learning has been widely applied to mammography-based breast cancer classification, with many studies reporting high diagnostic performance. However, substantial variability in datasets, validation strategies, and reporting practices complicates interpretation and clinical…
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Artikel Jurnal
An interpretable deep learning framework for multi-scale diagnosis of gastrointestinal conditions across adult and pediatric populations
The accurate and interpretable diagnosis of gastrointestinal (GI) conditions remains a significant clinical challenge, particularly across diverse populations such as adults and children. In this study, we propose a domain adaptive explainable Multi-Scale Convolutional Neural Network integrated…
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Distribution matching with discrete optimal transport flows: Modeling normative brain aging
Matching instances from distinct distributions without direct pairings is a common challenge in biology. Estimating individual trajectories in temporal phenomena can be seen as a form of distribution alignment. We propose a novel medical image-to-image translation method combining optimal transport…
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A lightweight memory-efficient 3D U-Net with attention and uncertainty modeling for coronary artery segmentation
Deep learning (DL) methods have been widely applied to 3D coronary artery segmentation. However, due to the large data volume of 3D CT images, efficient GPU memory utilization has become a major bottleneck for the practical deployment of current DL segmentation models. In addition, effectively…
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Artikel Jurnal
Evaluating artificial intelligence algorithms for the standardization of transtibial prosthetic socket shape design
The quality of a transtibial prosthetic socket depends on the prosthetist’s skills and expertise, as the fitting is performed manually. This study investigates multiple artificial intelligence (AI) approaches to help standardize transtibial prosthetic socket design. Data from 118 patients were…
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ModAn-MulSupCon: Modality- and anatomy-aware multi-label supervised contrastive pretraining for medical imaging
Background and objective:: Expert annotations limit large-scale supervised pretraining in medical imaging, while ubiquitous metadata (modality, anatomical region) remain underused. We introduce ModAn-MulSupCon, a modality- and anatomy-aware metadata-supervised pretraining scheme that applies an…
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Deep learning under severe class imbalance: A comparative study of CNNs and YOLO models for canine white blood cell classification
Automated white blood cell classification in veterinary medicine faces a significant challenge due to the extreme natural class imbalance in canine physiology. While deep convolutional neural networks (CNNs) have revolutionized human hematology, they often fail to recognize rare, clinically…
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Artikel Jurnal
X-GAN: Physics-informed generative model for edge-preserved chest X-ray image denoising
Noise-free X-ray imaging is crucial for accurate medical diagnostics. However, noise remains a persistent challenge, hindering diagnostic accuracy. This work introduces X-GAN, a physics-informed generative adversarial network (GAN) designed to denoise chest X-ray images while preserving critical…
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Formal methods for early prediction of frozen shoulder severity and recovery: A multicentre prospective study
Background: Frozen Shoulder (FS) is a pathological state resulting in severe pain and global functional limitations. Although diagnosis is primarily clinical, traditional imaging often lacks prognostic utility. However, to date, only conventional human evaluation of imaging has been explored, while…
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Medical named entity recognition via lattice-enhanced transfer learning with random attention
Medical texts are characterized by high acquisition costs, specialized terminology, imbalanced distribution, and limited annotated resources, all hindering the performance of named entity recognition (NER) tasks. Transfer learning(TL) offers a practical solution by leveraging models pre-trained on…
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Aligning language models with clinical expertise: Direct preference optimization for heart failure nursing documentation in critical care
Nursing documentation in intensive care units (ICUs) provides essential clinical intelligence but often suffers from inconsistent terminology, informal styles, and lack of standardization, challenges that are particularly critical in heart failure care. This study applies Direct Preference…
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Modeling Parkinson's disease progression from longitudinal voice biomarkers: A comparative study of statistical and neural mixed effects models
Predicting Parkinson's Disease (PD) progression is crucial for personalized treatment, and voice biomarkers offer a promising non-invasive method for tracking symptom severity through telemonitoring. However, analyzing this longitudinal data is challenging due to inherent within-subject…
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Predicting hematoma expansion in spontaneous hypertensive intracerebral hemorrhage using radiomics and machine learning: An ensemble approach
This study aimed to construct an effective model based on radiomics features and machine learning (ML) algorithms to predict hematoma expansion (HE) in spontaneous hypertensive intracerebral hemorrhage (SHICH) patients. This retrospective study included 85 patients with SHICH, of whom 64 had stable…
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Predicting clinical outcomes of chemical war veterans exposed to mustard gas using machine learning models
Introduction: During the eight-year Iran-Iraq war, Iran experienced 387 chemical attacks exposing approximately 100,000 individuals. This study focuses on all chemical veterans in the country specifically those exposed to mustard gas between 1982 and 1987. In modern medicine, comprehensive data…
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A systematic literature review of deep learning techniques for identification of ocular abnormalities: Current insights and future directions
Ocular abnormalities such as glaucoma, cataracts, and retinoblastoma pose significant global health challenges due to their con- tribution to vision loss and reduced quality of life. This review investigates the application of machine learning (ML) and deep learning (DL) for the detection and…
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A new architecture based on the Xception algorithm for pneumonia detection using medical image datasets
This study aims to improve the reliability of pneumonia detection from chest X-ray images by addressing the instability and performance variability observed in conventional CNNs, particularly the original Xception architecture, under different training conditions. An improved Xception-based model…