COMPUTERS IN BIOLOGY AND MEDICINE生物与医学中的计算机
COMPUTERS IN BIOLOGY AND MEDICINE(英文缩写 COMPUT BIOL MED),ISSN 0010-4825,eISSN 1879-0534,中文译名:生物与医学中的计算机 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-27 至 2026-09-27，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2020 | 4.589 | Q1 |
| 2021 | 6.698 | Q1 |
| 2022 | 7.700 | Q1 |
| 2023 | 7.000 | Q1 |
| 2024 | 6.300 | Q1 |
COMPUTERS IN BIOLOGY AND MEDICINE 最新收录文献
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2. An end-to-end deep learning approach for lung nodule segmentation and classification using LN-DETR and deep sequential convolutional harmonic networks.
PMID:日期:2026-09-23Detecting lung cancer at an early stage improves survival outcomes, whereas accurate segmentation and classification of lung nodules are fundamental to dependable diagnosis. Classical approaches encounter difficulties, like limited accuracy, data imbalance, and complex feature extraction, emphasizing the need for enhanced automated techniques to increase timely lung cancer diagnosis. Hence, a Deep Sequential Convolutional Neural Harmonic Network (DSeqCH-Net) is devised for lung nodule classification from Computed Tomography (CT) scans. Lung CT images are initially subjected to adaptive median filtering to remove unwanted noise. The enhanced images are then forwarded to the segmentation phase, where Lung Nodule Detection Transformer (LN-DETR) extracts the lung nodule regions using the proposed FusionLoss-LFC loss, formed by combining Lovász-Softmax, Cross-Entropy, and Focal losses. The segmented images are augmented using rotation, flipping, and resizing techniques to increase image variability. Feature extraction is then carried out to obtain the most discriminative image characteristics, which are subsequently used by a Sequential Convolutional Neural Network (SCNN) for lung cancer detection. Lastly, lung cancer classification is performed employing DSeqCH-Net. Additionally, DSeqCH-Net achieved the highest True Negative Rate (TNR), accuracy, and True Positive Rate (TPR) of 95.813%, 96.745%, and 97.643%.
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3. A novel Ziehl-Neelsen sputum smear image database and deep learning strategy for automated detection of tuberculosis bacilli.
PMID:日期:2026-09-23The microscopic detection of Mycobacterium tuberculosis bacilli in Ziehl-Neelsen (ZN)-stained sputum smears remains essential for tuberculosis (TB) diagnosis, but it is limited by inter-observer variability, high operational workload, and a shortage of qualified specialists. Deep learning (DL) has shown strong potential to support this task; however, progress is constrained by the limited availability of well-annotated datasets and the lack of standardized evaluation protocols. This study presents a comprehensive investigation of DL-based object detection methods for TB bacillus identification, comprising (i) a systematic comparison of seven detectors, (ii) the introduction of a new annotated ZN-stained dataset containing 500 images, and (iii) an image-partitioning strategy designed to mitigate the performance degradation caused by heterogeneous image resolutions. Three public datasets and the proposed dataset were evaluated using cross-validation and cross-dataset experiments. Among the seven methods tested, Faster R-CNN consistently achieved the highest overall precision, sensitivity, F1-score, and average precision across datasets. The proposed partitioning strategy significantly improved detection performance, particularly for large images. Cross-dataset experiments revealed limited generalization across laboratories, underscoring the importance of standardized datasets and context-aware training. Overall, the proposed dataset and experimental findings offer practical guidance for future research toward robust, clinically deployable AI-assisted TB diagnostic workflows.
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4. Multi-scale 3D CNN with imbalance-aware training for structural MRI-based six-class Alzheimer disease staging.
PMID:日期:2026-09-22Alzheimer's disease staging from structural magnetic resonance imaging (MRI) is challenging when clinically adjacent stages and severe class imbalance are considered simultaneously. This study evaluates six-class classification of cognitively normal, subjective memory complaint, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease using T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The final cohort included 3592 MRI volumes from 904 participants, with subjective memory complaint representing 3.79% of the data. We developed a parameter-compact three-dimensional convolutional neural network (3D CNN) with multi-scale volumetric feature extraction and imbalance-aware training using class weighting, focal loss with label smoothing, weighted sampling, and minority-class intensity perturbation. In the primary evaluation, partitioning was performed at the MRI ImageID (scan) level: a fixed test set of 719 MRI volumes was excluded from model development, while the remaining 2873 vol were used in five stratified training and validation folds. ImageIDs were mutually exclusive across partitions; however, participants were not grouped, and longitudinal scans from the same participant could therefore occur in different partitions. The proposed model achieved 0.9018 ± 0.0452 accuracy, 0.8804 ± 0.0393 macro F1, 0.8762 ± 0.0544 Matthews correlation coefficient (MCC), and 0.9463 ± 0.0056 macro area under the receiver operating characteristic curve (AUC) under this scan-level protocol. A same-data EfficientNet-B0 baseline achieved stronger aggregate performance, while the proposed model used 43.7% fewer parameters. Paired ablation analysis showed that weighted sampling did not improve aggregate metrics but increased subjective memory complaint precision, recall, and F1, with all corresponding 95% confidence intervals excluding zero. These findings support class-specific evaluation under severe class imbalance while distinguishing scan-level discrimination from participant-independent generalization.
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5. SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI.
PMID:日期:2026-09-22Muscle mass and muscle quality, characterized by intramuscular adiposity and fiber composition, are clinically established biomarkers significantly associated with obesity, sarcopenia, frailty and cardiometabolic disorders. Current muscle quantification efforts are predominantly based on CT, often focusing on single anatomical region such as the L3 vertebral level, due it its relatively standardized acquisition protocols and the overall simplicity of developing muscle segmentation algorithms. However, MRI offers superior soft-tissue contrast for visualizing intramuscular fat and enables muscle assessment across a wider range of body regions. In this study, we developed SegmentAnyMuscle, a model capable of segmenting muscles across diverse anatomical regions and various MRI sequences, addressing the barriers to clinical muscle assessment. By developing upon a robust, expert-curated dataset of 316 MRI exams from 160 patients covering 11 anatomical regions, SegmentAnyMuscle demonstrated high segmentation performance with an averaged Dice Similarity Coefficient (DSC) of 88.45% on routinely acquired MRI sequences and maintained strong performance (DSC of 86.21%) under clinical challenging cases, including less common sequences, abnormalities such as muscular atrophy, implant-associated artifacts, and significant noise. Furthermore, automatic skeletal muscle mass measurement provided by SegmentAnyMuscle showed excellent correlation with expert manual assessments (Pearson correlation coefficient >0.99, p < 0.001). By providing the first reliable, automatic muscle segmentation tool across diverse anatomical regions and MRI acquisition settings, SegmentAnyMuscle can open new opportunities for MRI-based muscle health assessment and encourages broader indicators of metabolic and functional health. We made codes and model publicly available at: https://github.com/mazurowski-lab/SegmentAnyMuscle.
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6. Non-contact estimation of divers physiological indicators using iPPG and attention U-Net mechanism.
PMID:日期:2026-09-22Ensuring diver safety remains a major challenge in breath-hold diving due to physiological stressors such as hypoxia and increased hydrostatic pressure. Assessing physiological parameters before and after diving enables characterization of baseline and post-dive variations associated with these stressors. This study presents an imaging photoplethysmography (iPPG)-based method to estimate vital parameters, aiming to develop a non-invasive monitoring framework for diver safety. The proposed framework is based on an Attention U-Net architecture for segmenting regions of interest in video recordings of nine breath-hold divers acquired before and after a 30-meter dive in the controlled thermal pool at Y-40 The Deep Joy, Italy. The segmentation model was trained on the Face and Skin Detection (FSD) dataset. iPPG signals were derived from pulsatile skin regions to estimate heart rate (HR) and blood oxygen saturation (SpO). The pipeline was first validated on the Pulse Rate Detection (PURE) dataset and then evaluated on the acquired diver data. The segmentation model achieved an accuracy of 97% and an intersection over union of 89% on test data. HR and SpO were estimated with mean absolute errors (MAEs) of 5 bpm and 4%, respectively, on the PURE dataset. The proposed pipeline further estimated pre-dive MAEs of 6 bpm for HR and 3% for SpO, while post-dive MAEs were 7 bpm for HR and 3% for SpO, indicating increased physiological variability following breath-hold diving. This study demonstrates the effects of breath-hold diving on physiological responses and establishes the feasibility of iPPG-based non-contact monitoring for detecting stress-related changes in divers.
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7. Rhamba: Region-aware hybrid attention-Mamba framework for self-supervised learning in resting-state fMRI.
PMID:日期:2026-09-21Self-supervised pretraining is promising for large-scale neuroimaging representation learning, yet the impact of region-aware masking and hybrid sequence modeling remains underexplored. In this work, we introduce Rhamba, a region-aware pretraining framework that integrates anatomically guided masking with hybrid Attention-Mamba architectures for resting state functional magnetic resonance imaging (fMRI) analysis. Models were pretrained on the ABIDE dataset using region-aligned patch embeddings and three masking strategies with increasing spatial specificity. We evaluated four architectural variants: a Mamba-dominant model, an Alternate architecture with interleaved Mamba and Attention blocks, and two hybrid encoder-decoder configurations (Attention-Mamba (AM) and Mamba-Attention (MA)). The pretrained models were fine-tuned on downstream classification tasks using the COBRE and ADHD-200 datasets for schizophrenia and attention-deficit/hyperactivity disorder discrimination. We employed Integrated Gradients, an explainable AI method, to identify the brain regions contributing to model predictions. Our masking strategy strongly influenced reconstruction behavior, with reconstruction loss following a consistent ordering (Any>Majority>Pure). However, this trend did not directly translate into downstream performance, where differences were modest and dataset-dependent. The hybrid architecture with the MA configuration achieved the highest average AUROC across both datasets. Rhamba demonstrated competitive performance against state-of-the-art methods. Region-wise analysis showed that peak performance depends on the interaction between masking strategy and architecture rather than a single dominant configuration. The hybrid architectures underscore the importance of combining global context modeling with efficient sequence dynamics. Overall, Rhamba offers a flexible framework for balancing interpretability, scalability, and performance in large-scale fMRI representation learning.
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8. A systemic neuroendocrine immune axis in breast cancer revealed by MMD regularized cross tissue latent alignment across four independent cohorts.
PMID:日期:2026-09-19Understanding systemic determinants of breast tumor immunity requires bridging transcriptomically distinct tissue compartments that cannot be sampled simultaneously in a single patient. We developed an MMD-regularized Domain Adaptation Autoencoder (DAA) to align unpaired RNA-seq profiles from GTEx neuroendocrine tissues (n=189) and TCGA-BRCA tumors (n=1391) within a shared 128-dimensional latent space, enabling the first cross-tissue transcriptomic interrogation of the neuroendocrine-breast tumor immune interface. The dominant cross-tissue axis was identified by Pearson correlation and rigorously validated by permutation testing (n=1000 iterations), then independently assessed in METABRIC microarray (n=1980) and SCAN-B RNA-seq (n=3273) cohorts via a strict gene-intersection protocol that eliminated zero-padding artefacts. The DAA achieved stable cross-domain alignment (mixing score =26.58%), and Latent Dimension 31 emerged as a significant systemic immune-inflammatory axis (p=0.001; aggregate correlation 13.9× above the permutation null), driven by T-cell receptor variable chains, immunoglobulin genes, and the tolerogenic phospholipase PLA2G2D. METABRIC validation recovered a mechanistically concordant acute-phase secretory signature (LBP, SAA1, PLA2G2A), while SCAN-B confirmed PLA2G2D and CCL18 on a unified cross-platform latent axis. The latent score significantly stratified overall survival (p=0.0036) and relapse-free survival (p=0.0084), and precisely reproduced the established breast cancer immune topology across all six molecular subtypes (Kruskal-Wallis H=139.4, p<0.0001). External validation in the independent neoadjuvant GEO cohort GSE25066 (n=508; Affymetrix GPL96) via a Strict Intersection Protocol (604-gene intersection, zero-padding eliminated) confirmed axis recovery (Latent Dimension 115; PLA2G2D |r|=0.266), significant distant relapse-free survival stratification (log-rank p=0.022), and non-significant pathological complete response to chemotherapy (p=0.241), establishing the axis as a prognostic but not predictive biomarker. Functional annotation in GSE25066 revealed significant correlation with all 12 curated immune cell signatures (Spearman ρ=0.10-0.42; all p<0.05), and GSEA pre-ranked analysis across 13,236 genes identified 34 significantly enriched Hallmark pathways (FDR < 0.25), led by Interferon Gamma Response (NES =2.90) and opposed by Estrogen Response Early (NES =-2.71). These findings, validated across 7152 patients in four independent cohorts, provide a computational transcriptomic framework linking systemic neuroendocrine regulation to breast tumor immunobiology and nominate PLA2G2D, SAA1, and LBP as candidate circulating biomarkers warranting prospective proteomic validation.
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9. The impact of regularisation methods for ECGI reconstructions during regular rhythms in an animal torso-tank model.
PMID:日期:2026-09-17Electrocardiographic imaging (ECGI) is a promising non-invasive technique that reconstructs epicardial potentials by combining high-density body-surface recordings with patient-specific 3D geometries. To systematically compare the performance of the main ECGI regularisation methods, an experimental setup was developed using isolated Langendorff-perfused rabbit hearts. Panoramic optical mapping, epicardial electrograms, and torso-tank signals were acquired simultaneously during atrial sinus rhythm and ventricular tachycardia. A tailored pre-processing pipeline was applied prior to inverse reconstruction using multiple methods, including Tikhonov (orders 0-2), truncated singular value decomposition (TSVD), damped singular value decomposition (DSVD), generalised minimal residual (GMRES), and Bayesian approaches. Results showed that no single method was universally optimal, with performance strongly dependent on cardiac region, rhythm, and evaluation metric. Tikhonov regularisations achieved the highest waveform similarity, reaching mean cross-correlation (CC) values up to 0.84 in the right atrium during sinus and 0.83 during ventricular tachycardia, though performance varied across orders and regions. In contrast, TSVD- and DSVD-based approaches yielded lower correlations (typically 0.62-0.78). Second-order Tikhonov achieved the lowest localisation error during ventricular tachycardia (6.77 ± 3.65 mm), while GMRES offered a competitive balance between spatial precision (7.04 ± 2.36 mm) and temporal accuracy. Bayes showed the highest CC variability across electrodes. Despite these differences, all methods consistently preserved dominant activation frequencies found in the measured signals (≈1.7 Hz in sinus rhythm and ≈4.8 Hz in tachycardia).
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10. EA-DualGAT: An error-aware dual-graph attention framework for Parkinson's disease diagnosis from wearable plantar pressure gait signals.
PMID:日期:2026-09-15Timely diagnosis of Parkinson's disease (PD) is essential for timely intervention and effective disease management. Wearable sensing technologies provide objective gait measurements and have become a promising, low-cost solution for automated PD diagnosis. However, existing deep learning and graph-based methods primarily focus on individual gait representations or fixed graph structures, often overlooking relationships among gait samples, difficult samples, and adaptive graph refinement during training. To address these limitations, this study proposes EA-DualGAT, an Error-Aware Dual Graph Attention Network (GAT) for PD diagnosis using wearable gait signals. A time-series autoencoder is first employed to extract latent representations from gait cycles acquired by 16 plantar pressure sensors measuring vertical ground reaction force (vGRF). These latent representations are subsequently used as graph node features to construct a gait-cycle similarity graph that captures structural dependencies among gait cycles. The proposed architecture combines several components: a residual multi-head GAT with Jumping Knowledge (JK) aggregation, a dynamically constructed Error Graph processed by a dedicated Error-GAT encoder, an Error Memory Bank (EMB), attention-based feature fusion, and self-distillation using an exponential moving average (EMA) teacher. These components work together to progressively refine difficult samples and improve representation learning. Experimental results demonstrate the effectiveness of the proposed framework, achieving 99.87% accuracy, 99.92% precision, 99.82% recall, and 99.87% F1-score on the test set. These findings indicate that EA-DualGAT provides an accurate and computationally efficient framework for wearable sensor-based PD diagnosis on the evaluated dataset, highlighting its potential for deployment in intelligent clinical decision-support systems and remote healthcare applications.