Computerized Medical Imaging and Graphics最新文献

{"title":"LCBTS-Net: A lightweight cascaded 3D brain tumor segmentation network in magnetic resonance imaging","authors":"Ayse Bastug Koc ,&nbsp;Devrim Akgun","doi":"10.1016/j.compmedimag.2026.102727","DOIUrl":"10.1016/j.compmedimag.2026.102727","url":null,"abstract":"<div><div>Automatic segmentation of brain tumors from Magnetic Resonance (MR) images is crucial for diagnosing and developing personalized treatment plans. Nevertheless, inter-patient variability in tumor location and size makes automatic MR image segmentation a challenging problem. This study introduces a deep learning-based multi-scale automatic 3D lightweight and cascaded model to accurately separate tumorous regions from healthy tissues. The lightweight and cascaded architecture helps minimize the risk of overfitting and offers more stable and reliable predictions. Since each MR sequence highlights different anatomical and pathological features, the model combines these channels and feeds them into the network encoder to utilize all available information. The proposed model employs a multi-step cascaded estimation method that acknowledges the hierarchical structure of brain tumor substructures, breaking them down into regions from coarse to fine detail. During segmentation estimation, the result of the whole tumor region (WT) is used as a priori information for tumor core (TC) and enhancing tumor region (ET) estimation to guide the finer segmentation process. This lightweight model contains only 1.58 Million (M) parameters and 247.09 Giga floating-point operations per second (GFLOPs) for each region, making it a convenient solution for real-world clinical applications, especially in limited environments. The model achieved promising results on the BRATS 2020 dataset with Dice scores of 0.9285, 0.8871, 0.8694, and Hausdorff95 distances of 4.26, 6.46, 4.55 for WT, TC, and ET in the test set, respectively. These findings indicate that the proposed approach significantly improves the segmentation performance of brain tumor regions compared to existing state-of-the-art techniques.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"129 ","pages":"Article 102727"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147285767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of CT artifact reduction: From traditional reduction techniques to deep learning methods","authors":"Kai Chen ,&nbsp;Yinuo Wang ,&nbsp;Shuya Ji ,&nbsp;Cai Meng ,&nbsp;Chao Pan ,&nbsp;Zhouping Tang ,&nbsp;Diansheng Chen","doi":"10.1016/j.compmedimag.2026.102728","DOIUrl":"10.1016/j.compmedimag.2026.102728","url":null,"abstract":"<div><div>Computed Tomography (CT) artifact reduction aims to restore image structures degraded by diverse sources, including metal implants, patient motion, and detector imperfections, and is essential for maintaining diagnostic reliability. Despite substantial progress and the rapid emergence of deep learning-based approaches, a unified and well-structured synthesis of existing techniques remains lacking. To address this gap, this review classifies CT artifacts into ring, metal, motion, and scatter types, and provides a structured overview of their underlying physical mechanisms and corresponding imaging manifestations. Following the complete CT imaging chain, from hardware acquisition and system calibration to projection- and image-domain correction, we provide an in-depth review of traditional reduction strategies and establish a structured framework for classical techniques. Subsequently, deep learning-based artifact reduction methods are grouped into supervised, semi-supervised, and unsupervised paradigms, with representative studies reviewed in terms of their methodological contributions and inherent limitations. Finally, this review discusses the clinical applications of existing methods, highlights critical challenges, and outlines several promising directions for further research.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"129 ","pages":"Article 102728"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147311987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ME-Mamba: Multi-Expert Mamba with efficient knowledge capture and fusion for multimodal survival analysis","authors":"Chengsheng Zhang ,&nbsp;Linhao Qu ,&nbsp;Xiaoyu Liu ,&nbsp;Zhijian Song","doi":"10.1016/j.compmedimag.2026.102733","DOIUrl":"10.1016/j.compmedimag.2026.102733","url":null,"abstract":"<div><div>Multimodal survival analysis integrating Whole Slide Images (WSIs) and genomics is critical for precision oncology but remains challenged by the quadratic computational complexity and noise susceptibility of Transformer-based fusion mechanisms. To address these problems, we present ME-Mamba, a Multi-Expert Mamba framework that achieves linear complexity while enabling robust cross-modal interaction. Specifically, we propose a novel attention-guided scan strategy to mitigate the sequential scanning bias inherent in Mamba, ensuring the prioritization of discriminative features regardless of spatial order. Furthermore, we introduce a Synergistic Expert equipped with a parameter-free, dual-granularity fusion mechanism. By combining Optimal Transport (OT) for precise local alignment and Maximum Mean Discrepancy (MMD) for global distribution consistency, our approach acts as a hard filter to maximize the signal-to-noise ratio while preventing overfitting. Extensive experiments on five TCGA datasets demonstrate that ME-Mamba significantly outperforms state-of-the-art methods in both prediction accuracy and computational efficiency. We will make our code publicly available.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"129 ","pages":"Article 102733"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147285778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM2image: A novel framework for accurate diagnosis of diarrhea viruses using pathological images and semantic information","authors":"Liangliang Liu,&nbsp;Zizhao Niu,&nbsp;Fengjie Zhao,&nbsp;Longxian Zhang","doi":"10.1016/j.compmedimag.2026.102735","DOIUrl":"10.1016/j.compmedimag.2026.102735","url":null,"abstract":"<div><div>Accurate diagnosis of diarrheal viruses from histopathological images is critical for veterinary medicine and animal disease control. Existing deep learning approaches, however, often rely solely on visual features, lacking integration of clinical semantics and struggling with limited annotated data. To overcome these limitations, we propose LLM2image, a multimodal framework that enhances pathological image classification by integrating visual features with semantically rich textual descriptions generated by large language models (LLMs). The framework comprises: (1) a pixel-level MLP encoder for image representation, (2) GPT-4.0 for generating class-specific pathological descriptions, and (3) a cross-modal fusion transformer that aligns visual and textual features via multi-head attention. Evaluated on an in-house dataset of 516 porcine intestinal images across four categories healthy, ETEC, PDCoV, and PoRVour method achieved an accuracy of 89.51%, surpassing state-of-the-art models and matching the diagnostic performance of veterinarians with 10 years of experience. External validation on the public PAIP2020 colorectal cancer dataset further confirmed its generalizability, with 87.42% accuracy. Ablation studies and visual attention analysis demonstrate that the inclusion of LLM-generated text significantly improves both classification accuracy and interpretability. The model has been deployed as a lightweight Android application, supporting offline rapid diagnosis, highlighting its potential for real-world veterinary and medical applications.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"129 ","pages":"Article 102735"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147285760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pseudo-label-free instance screening of non-tumor regions in whole slide images for improved classification and survival prediction","authors":"Yujie Zhao ,&nbsp;Hengtao Ding ,&nbsp;Xiangyan Zhang ,&nbsp;Xue Zhao ,&nbsp;Shuchao Zhao ,&nbsp;Ping Yang ,&nbsp;Dapeng Wang ,&nbsp;Shudong Wang ,&nbsp;Xiaoming Xing","doi":"10.1016/j.compmedimag.2026.102738","DOIUrl":"10.1016/j.compmedimag.2026.102738","url":null,"abstract":"<div><div>Multiple instance learning (MIL) regards a whole slide image (WSI) as a bag, from which image instances (patches) are cropped to extract features, thereby addressing the challenge that the ultra-high resolution of WSIs makes direct feature extraction infeasible. However, in positive WSIs (malignant or tumor), negative (benign or normal tissue) instances can significantly impair the effectiveness of feature extraction, and due to the high cost of labeling a large number of instances, it is currently difficult to construct well-annotated datasets to train highly accurate instance classifiers for filtering negative instances. To address this limitation, we propose PGPFMIL, a MIL framework that removes negative instances under the guidance of prior prototypes. The core idea is based on the property that negative WSIs never contain positive instances, thus leveraging a small set of negative WSIs as a support set with instance-level annotations, and applying prototype learning to classify instances and filter noisy ones in positive WSIs. Furthermore, to compensate for the potential loss of spatial information between instances caused by the filtering module, we incorporate an instance spatial–semantic fusion module and an attention module based on deficiency compensation, which supplement possibly misfiltered positive instances. We conduct experiments on one in-house dataset and three public datasets, and extensive results demonstrate that our method consistently outperforms prior state-of-the-art models across multiple tasks while showing superior generalization ability.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"129 ","pages":"Article 102738"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147322267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain tumor classification model guided by class activation mapping","authors":"Yuqi Ma ,&nbsp;Wang Zhang ,&nbsp;Yaoyao Feng ,&nbsp;Maoling Peng ,&nbsp;Shi Tang ,&nbsp;Yongjun Zhu ,&nbsp;Hailing Xiong ,&nbsp;Shanxiong Chen","doi":"10.1016/j.compmedimag.2026.102726","DOIUrl":"10.1016/j.compmedimag.2026.102726","url":null,"abstract":"<div><h3>Background</h3><div>Brain tumors present significant risks to human health, with various types differing in treatment and prognosis. Accurate diagnosis is essential for improving survival rates. Multimodal Magnetic Resonance Imaging plays a key role in tumor identification but faces challenges such as similar intensity distributions across modalities, tumor morphological variability, and indistinct boundaries. Existing classification methods often lack interpretability, which is crucial for clinical decision-making.</div></div><div><h3>Methods</h3><div>This study proposes a brain tumor classification model that integrates Class Activation Mapping techniques to enhance interpretability. The model generates stable class activation maps through end-to-end training, which act as weak supervision signals for tumor localization. Key components include a Saliency Learning Module for improved attention weight generation, a Sample Selection Module for better sample differentiation, and a Balanced Perception Loss function to guide model training.</div></div><div><h3>Results</h3><div>The proposed model achieved classification accuracies ranging from 96% to 99%, averaging 97.41% across ten-fold cross-validation. Precision, recall, and F1 score averaged 97.53%, 97.66%, and 97.58%, respectively. These results demonstrate robust performance in differentiating various brain tumor types. The model’s use of class activation maps enhances its interpretability, enabling visualization of the decision-making process and pinpointing tumor regions. Additionally, the model performed better than other methods in comparative experiments.</div></div><div><h3>Conclusions</h3><div>Our brain tumor classification model improves accuracy and interpretability. It provides a significant advancement in brain tumor diagnosis by accurately localizing tumor regions and facilitating clinicians’ understanding of model predictions.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"129 ","pages":"Article 102726"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-preserving multimodal fusion for Alzheimer’s staging: A federated vision transformer framework with explainable AI","authors":"Marwa Ben Gara Ali,&nbsp;Abir Smiti","doi":"10.1016/j.compmedimag.2026.102730","DOIUrl":"10.1016/j.compmedimag.2026.102730","url":null,"abstract":"<div><div>Accurate, early-stage staging of Alzheimer’s disease (AD) is critical for therapeutic intervention but is hampered by data privacy regulations, multimodal data heterogeneity, and the “black-box” nature of complex Artificial Intelligence (AI) models. To address these interconnected challenges, we introduce a novel, privacy-preserving federated learning framework for robust and interpretable AD staging. Our method integrates four clinically relevant modalities, 3D structural Magnetic Resonance Imaging (MRI), Image Biomarker Standardization Initiative (IBSI)-compliant radiomics, cognitive assessments, and U.S. Food and Drug Administration (FDA) cleared plasma biomarkers, within a parameter-efficient Swin-UNet transformer architecture. Key innovations include: (1) Low-Rank Adaptation (LoRA) and dynamic token gating, reducing communication overhead by 99% (to 140 KB/round) compared to standard federated averaging; (2) A hierarchical attention fusion mechanism that dynamically weights modalities based on predictive uncertainty; and (3) A fuzzy-rough hybrid explainability pipeline that synthesizes client-specific saliency maps into a consensus-driven, clinically coherent interpretation without sharing raw data. Evaluated on a stratified four-client federation derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, our framework achieved a state-of-the-art balanced accuracy of 80.7%, closely approaching the centralized upper bound (82.1%) while demonstrating superior robustness to simulated domain shifts and consistent cross-site interpretability (Dice similarity: 0.84). This work establishes a foundational blueprint for the next generation of healthcare AI systems that are simultaneously accurate, efficient, privacy-preserving, and trustworthy, enabling scalable collaboration across distributed clinical networks.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"129 ","pages":"Article 102730"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion-based virtual multi-stain staining for pituitary adenoma histopathology","authors":"Yifan Chen ,&nbsp;Nidan Qiao ,&nbsp;Xinyuan Niu ,&nbsp;Yao Zhao ,&nbsp;Jinhua Yu ,&nbsp;Guoqing Wu ,&nbsp;Haixia Cheng","doi":"10.1016/j.compmedimag.2026.102723","DOIUrl":"10.1016/j.compmedimag.2026.102723","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Immunohistochemistry (IHC) for lineage-defining transcription factors—such as Pit1, Tpit, and SF1—together with markers like Ki67 and ACTH, is central to classifying pituitary neuroendocrine tumors and guiding management. Yet preparing multiple IHC slides beyond hematoxylin–eosin (H&amp;E) is costly and inconsistent across sites. We aim to produce clinically reliable virtual IHC stains from a single H&amp;E image under unpaired supervision.</div></div><div><h3>Methods:</h3><div>We develop a diffusion-based one-to-many virtual staining framework that transforms a single H&amp;E image into five key IHC stains. Our approach fuses multi-scale structural features into a unified conditional representation, processed by a shared backbone with stain-specific decoders to enable mutually reinforcing learning across stains. The model incorporates perception-prioritized, timestep-aware diffusion loss and a stain-consistency term during training, combined with classifier-guided sampling during inference, to achieve exceptional fidelity in both cellular structure and chromogenic style. For clinical validation, we further introduced the nuclear positivity rate as a slide-level endpoint to quantitatively evaluate agreement between virtual and real IHC.</div></div><div><h3>Results and Conclusion:</h3><div>Compared to the best-performing baseline, our method achieves superior pixel and perceptual fidelity (PSNR <span><math><mi>↑</mi></math></span> +0.94 dB, SSIM <span><math><mi>↑</mi></math></span> +0.015; FID <span><math><mi>↓</mi></math></span> <span><math><mrow><mo>−</mo><mn>7</mn><mo>.</mo><mn>8</mn><mtext>%</mtext></mrow></math></span>, LPIPS <span><math><mi>↓</mi></math></span> <span><math><mrow><mo>−</mo><mn>7</mn><mo>.</mo><mn>9</mn><mtext>%</mtext></mrow></math></span>). For the clinical endpoint, averaged across all stains, it delivers substantial improvements in mean absolute error, correlation, and accuracy relative to the baseline average—reducing MAE by approximately 28.7%, increasing correlation by 0.07, and boosting accuracy by 0.05. Coupled with roughly 80% fewer parameters and comparable throughput, these outcomes position our approach as a unified, clinically focused diffusion model for virtual multi-stain synthesis.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"129 ","pages":"Article 102723"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated 3D cephalometry: A lightweight V-net for landmark localization on CBCT","authors":"Benedetta Baldini ,&nbsp;Giulia Rubiu ,&nbsp;Marco Serafin ,&nbsp;Marco Bologna ,&nbsp;Giuseppe Maurizio Facchi ,&nbsp;Giuseppe Baselli ,&nbsp;Gianluca Martino Tartaglia","doi":"10.1016/j.compmedimag.2026.102700","DOIUrl":"10.1016/j.compmedimag.2026.102700","url":null,"abstract":"<div><div>Cephalometric analysis is a widely adopted procedure for clinical decision support in orthodontics. It involves manual identification of predefined anatomical landmarks on three-dimensional cone beam CT scans, followed by the computation of linear and angular measurements. To reduce processing time and operator dependency, this study aimed to develop a light-weight deep learning (DL) model capable of automatically localizing 16 anatomically defined landmarks. To ensure model robustness and generalizability, the model was trained on a dataset of 350 manually annotated CBCT scans acquired from various imaging systems, covering a wide range of patient ages and skeletal classifications. The trained model is a V-net, optimized for practical use in clinical workflows. The model achieved a mean localization error of 1.95 ± 1.06 mm, which falls within the clinically acceptable threshold of 2 mm. Moreover, the predicted landmarks were used to calculate cephalometric measurements and compare with manually derived values. The resulting errors was −0.15 ± 0.95° for angular measurements and 0.20 ± 0.28 mm for linear ones, with Bland–Altman analysis demonstrating strong agreement and acceptable variability. These results suggest that automated measurements can reliably replace manual ones. Given the clinical relevance of cephalometric parameters - particularly the ANB angle, which is critical for skeletal classification and orthodontic treatment planning - this model represents a promising clinical decision support tool. Additionally, its low computational complexity enables fast prediction, with mean inference time lower than 32 s per scan, promoting its integration into routine clinical settings due to both technical feasibility and robustness across heterogeneous datasets.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102700"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning geometric and visual features for medical image segmentation with vision GNN","authors":"Xinhong Li ,&nbsp;Geng Chen ,&nbsp;Yuanfeng Wu ,&nbsp;Haotian Jiang ,&nbsp;Tao Zhou ,&nbsp;Yi Zhou ,&nbsp;Wentao Zhu","doi":"10.1016/j.compmedimag.2026.102720","DOIUrl":"10.1016/j.compmedimag.2026.102720","url":null,"abstract":"<div><div>As a fundamental task, medical image segmentation plays a crucial role in various clinical applications. In recent years, deep learning-based segmentation methods have achieved significant success. However, these methods typically represent the image and objects within it as grid-structural data, while insufficient attention is given to relationships between the objects to segment. To address this issue, we propose a novel model called MedSegViG, which consists of a hierarchical encoder based on Vision GNN (ViG) and a hybrid feature decoder. During the segmentation process, our model first represents the image as a graph and then utilizes the encoder to extract multi-level graph features and image features. Finally, our hybrid feature decoder fuses these features to generate the final segmentation map. To validate the effectiveness of the proposed model, we conducted extensive experiments on six datasets across three types of lesions: polyps, skin lesions, and retinal vessels. The results demonstrate that MedSegViG achieves superior segmentation accuracy, robustness, and generalizability.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"128 ","pages":"Article 102720"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}