Computer methods and programs in biomedicine最新文献

{"title":"M2OCNN: Many-to-One Collaboration Neural Networks for simultaneously multi-modal medical image synthesis and fusion","authors":"Jian Zhang,&nbsp;Xianhua Zeng","doi":"10.1016/j.cmpb.2025.108612","DOIUrl":"10.1016/j.cmpb.2025.108612","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Acquiring comprehensive information from multi-modal medical images remains a challenge in clinical diagnostics and treatment, due to complex inter-modal dependencies and missing modalities. While cross-modal medical image synthesis (CMIS) and multi-modal medical image fusion (MMIF) address certain issues, existing methods typically treat these as separate tasks, lacking a unified framework that can generate both synthesized and fused images in the presence of missing modalities.</div></div><div><h3>Methods:</h3><div>In this paper, we propose the Many-to-One Collaboration Neural Network (M2OCNN), a unified model designed to simultaneously address CMIS and MMIF. Unlike traditional approaches, M2OCNN treats fusion as a specific form of synthesis and provides a comprehensive solution even when modalities are missing. The network consists of three modules: the Parallel Untangling Hybrid Network, Comprehensive Feature Router, and Series Omni-modal Hybrid Network. Additionally, we introduce a mixed-resolution attention mechanism and two transformer variants, Coarsormer and ReCoarsormer, to suppress high-frequency interference and enhance model performance.</div><div>M2OCNN outperformed state-of-the-art methods on three multi-modal medical imaging datasets, achieving an average PSNR improvement of 2.4 dB in synthesis tasks and producing high-quality fusion images despite missing modalities. The source code is available at <span><span>https://github.com/zjno108/M2OCNN</span><svg><path></path></svg></span>.</div></div><div><h3>Conclusion:</h3><div>M2OCNN offers a novel solution by unifying CMIS and MMIF tasks in a single framework, enabling the generation of both synthesized and fused images from a single modality. This approach sets a new direction for research in multi-modal medical imaging, with implications for improving clinical diagnosis and treatment.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108612"},"PeriodicalIF":4.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094871","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":"Semi-supervised Strong-Teacher Consistency Learning for few-shot cardiac MRI image segmentation","authors":"Yuting Qiu ,&nbsp;James Meng ,&nbsp;Baihua Li","doi":"10.1016/j.cmpb.2025.108613","DOIUrl":"10.1016/j.cmpb.2025.108613","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cardiovascular disease is a leading cause of mortality worldwide. Automated analysis of heart structures in MRI is crucial for effective diagnostics. While supervised learning has advanced the field of medical image segmentation, it however requires extensive labelled data, which is often limited for cardiac MRI.</div></div><div><h3>Methods:</h3><div>Drawing on the principle of consistency learning, we introduce a novel semi-supervised Strong-Teacher Consistency Network for few-shot multi-class cardiac MRI image segmentation, leveraging largely available unlabelled data. This model incorporates a student–teacher architecture. A multi-teacher structure is introduced to learn diverse perspectives and avoid local optimals when dealing with largely varying cardiac structures and anatomical features. It employs a hybrid loss that emphasizes consistency between student and teacher representations, alongside supervised losses (e.g., Dice and Cross-entropy), tailored to the challenge of unlabelled data. Additionally, we introduced feature-space virtual adversarial training to enhance robust feature learning and model stability.</div></div><div><h3>Results:</h3><div>Evaluation and ablation studies on the MM-WHS and ACDC benchmark datasets show that the proposed model outperforms nine state-of-the-art semi-supervised methods, particularly with limited annotated data. It achieves 90.14% accuracy on MM-WHS and 78.45% accuracy on ACDC at labelling rates of 25% and 1%, respectively. It also highlights its unique advantages over fully-supervised and single-teacher approaches.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108613"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-experimental rapid identification of lower respiratory tract infections in patients with chronic obstructive pulmonary disease using multi-label learning","authors":"Hangzhi He ,&nbsp;Hui Zhao ,&nbsp;Lifang Li ,&nbsp;Hong Yang ,&nbsp;Jingjing Yan ,&nbsp;Yiwei Yuan ,&nbsp;Xiangwen Hu ,&nbsp;Yanbo Zhang","doi":"10.1016/j.cmpb.2025.108618","DOIUrl":"10.1016/j.cmpb.2025.108618","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Microbiological culture is a standard diagnostic test that takes a long time to identify lower respiratory tract infections (LRTI) in patients with chronic obstructive pulmonary disease (COPD). This study entailed the development of an interactive decision-support system using multi-label machine learning. It is designed to assist clinical medical staff in the rapid and simultaneous diagnosis of various infections in these patients.</div></div><div><h3>Methods</h3><div>Clinical health record data were collected from inpatients with COPD suspected of having a LRTI. Two major categories of multi-label learning frameworks were integrated with various machine learning algorithms to create 23 predictive models to identify four categories of infection: fungal, gram-negative bacterial, gram-positive bacterial, and multidrug-resistant organism infections. The predictive power of the individual models was tested. Subsequently, the model with the highest comprehensive performance was selected and integrated with SHAP technology to construct a decision support system.</div></div><div><h3>Results</h3><div>Three-thousand-eight-hundred-one subjects participated in this study. LP-RF recorded the highest overall performance, with a Hamming loss of 0.158 (95 %<em>CI</em>: 0.157–0.159) and a samples-precision of 0.894 (95 %<em>CI</em>: 0.891–0.896). The developed diagnostic decision support system generates predicted probability output for each infection category in a specific patient and displays the interpreted output results.</div></div><div><h3>Conclusion</h3><div>The developed multi-label decision support system enables effective prediction of four categories of infections in patients with a history of COPD, and has the potential to curb the overuse of antimicrobial drugs. This system is highly explainable and interactive, providing real-time support in the simultaneous diagnosis of multiple infection categories.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108618"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SeLa-MIL: Developing an instance-level classifier via weakly-supervised self-training for whole slide image classification","authors":"Yingfan Ma ,&nbsp;Mingzhi Yuan ,&nbsp;Ao Shen ,&nbsp;Xiaoyuan Luo ,&nbsp;Bohan An ,&nbsp;Xinrong Chen ,&nbsp;Manning Wang","doi":"10.1016/j.cmpb.2025.108614","DOIUrl":"10.1016/j.cmpb.2025.108614","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Pathology image classification is crucial in clinical cancer diagnosis and computer-aided diagnosis. Whole Slide Image (WSI) classification is often framed as a multiple instance learning (MIL) problem due to the high cost of detailed patch-level annotations. Existing MIL methods primarily focus on bag-level classification, often overlooking critical instance-level information, which results in suboptimal outcomes. This paper proposes a novel semi-supervised learning approach, SeLa-MIL, which leverages both labeled and unlabeled instances to improve instance and bag classification, particularly in hard positive instances near the decision boundary.</div></div><div><h3>Methods</h3><div>SeLa-MIL reformulates the traditional MIL problem as a novel semi-supervised instance classification task to effectively utilize both labeled and unlabeled instances. To address the challenge where all labeled instances are negative, we introduce a weakly supervised self-training framework by solving a constrained optimization problem. This method employs global and local constraints on pseudo-labels derived from positive WSI information, enhancing the learning of hard positive instances and ensuring the quality of pseudo-labels. The approach can be integrated into end-to-end training pipelines to maximize the use of available instance-level information.</div></div><div><h3>Results</h3><div>Comprehensive experiments on synthetic datasets, MIL benchmarks, and popular WSI datasets demonstrate that SeLa-MIL consistently outperforms existing methods in both instance and bag-level classification, with substantial improvements in recognizing hard positive instances. Visualization further highlights the method’s effectiveness in pathology regions relevant to cancer diagnosis.</div></div><div><h3>Conclusion</h3><div>SeLa-MIL effectively addresses key challenges in MIL-based WSI classification by reformulating it as a semi-supervised problem, leveraging both weakly supervised learning and pseudo-labeling techniques. This approach improves classification accuracy and generalization across diverse datasets, making it valuable for pathology image analysis.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108614"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143294124","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":"Evaluation of mandibular advancement surgery efficacy in treating obstructive sleep apnea: A study on turbulence kinetic energy","authors":"Mohd Faruq Abdul Latif ,&nbsp;Nik Nazri Nik Ghazali ,&nbsp;Shaifulazuar Rozali ,&nbsp;Irfan Anjum Badruddin ,&nbsp;Sarfaraz Kamangar","doi":"10.1016/j.cmpb.2025.108610","DOIUrl":"10.1016/j.cmpb.2025.108610","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Obstructive sleep apnoea (OSA) is a prevalent sleep disease characterised by recurrent airway obstruction during sleep, resulting in diminished oxygen intake and disrupted sleep patterns. This study investigates the effectiveness of mandibular advancement surgery as a surgical intervention for obstructive sleep apnoea by analysing the postoperative alterations in turbulence kinetic energy (TKE).</div></div><div><h3>Methodology</h3><div>The research involved five subjects receiving mandibular advancement surgery (MAS). The quantification of TKE was performed both before and throughout the method using a combination of computational fluid dynamics (CFD) models and empirical measurements. A suitable grid size of 2.6 million cells for CFD simulations was determined by grid sensitivity analysis and corroborated with physical measurements.</div></div><div><h3>Results</h3><div>The findings indicated a significant increase in TKE for each individual post-procedure, with increments varying from 23 % to 460 %. The elevated TKE indicates a more rapid airflow in the upper airway post-surgery. This is probably attributable to alterations in the airway's morphology resulting from the surgery. The observed rise in speed and turbulence is theoretically supported by Bernoulli's principle, which elucidates the relationship between air flow velocity and the pressure it generates.</div></div><div><h3>Conclusions</h3><div>This study demonstrates that mandibular advancement surgery efficiently alleviates OSA by markedly enhancing airflow and diminishing turbulence in the upper airway post-treatment. The use of physical validation and grid sensitivity analysis in computational fluid dynamics simulations underscores the meticulous technique utilised, offering a comprehensive assessment of the efficacy of the surgical interventions for OSA.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108610"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143294125","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":"Semantic-driven synthesis of histological images with controllable cellular distributions","authors":"Alen Shahini ,&nbsp;Alessandro Gambella ,&nbsp;Filippo Molinari ,&nbsp;Massimo Salvi","doi":"10.1016/j.cmpb.2025.108621","DOIUrl":"10.1016/j.cmpb.2025.108621","url":null,"abstract":"<div><div>Digital pathology relies heavily on large, well-annotated datasets for training computational methods, but generating such datasets remains challenging due to the expertise required and inter-operator variability. We present SENSE (SEmantic Nuclear Synthesis Emulator), a novel framework for synthesizing realistic histological images with precise control over cellular distributions. Our approach introduces three key innovations: (1) A statistical modeling system that captures class-specific nuclear characteristics from expert annotations, enabling generation of diverse yet biologically plausible semantic content; (2) A hybrid ViT-Pix2Pix GAN architecture that effectively translates semantic maps into high-fidelity histological images; and (3) A modular design allowing independent control of cellular properties including type, count, and spatial distribution. Evaluation on the MoNuSAC dataset demonstrates that SENSE generates images matching the quality of real samples (MANIQA: 0.52 ± 0.03 vs 0.52 ± 0.04) while maintaining expert-verified biological plausibility. In segmentation tasks, augmenting training data with SENSE-generated images improved overall performance (DSC from 79.71 to 84.86) and dramatically enhanced detection of rare cell types, with neutrophil segmentation accuracy increasing from 40.18 to 78.71 DSC. This framework enables targeted dataset enhancement for computational pathology applications while offering new possibilities for educational and training scenarios requiring controlled tissue presentations.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108621"},"PeriodicalIF":4.9,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PMFSNet: Polarized multi-scale feature self-attention network for lightweight medical image segmentation","authors":"Jiahui Zhong ,&nbsp;Wenhong Tian ,&nbsp;Yuanlun Xie ,&nbsp;Zhijia Liu ,&nbsp;Jie Ou ,&nbsp;Taoran Tian ,&nbsp;Lei Zhang","doi":"10.1016/j.cmpb.2025.108611","DOIUrl":"10.1016/j.cmpb.2025.108611","url":null,"abstract":"<div><h3>Background and objectives:</h3><div>Current state-of-the-art medical image segmentation methods prioritize precision but often at the expense of increased computational demands and larger model sizes. Applying these large-scale models to the relatively limited scale of medical image datasets tends to induce redundant computation, complicating the process without the necessary benefits. These approaches increase complexity and pose challenges for integrating and deploying lightweight models on edge devices. For instance, recent transformer-based models have excelled in 2D and 3D medical image segmentation due to their extensive receptive fields and high parameter count. However, their effectiveness comes with the risk of overfitting when applied to small datasets. It often neglects the vital inductive biases of Convolutional Neural Networks (CNNs), essential for local feature representation.</div></div><div><h3>Methods:</h3><div>In this work, we propose PMFSNet, a novel medical imaging segmentation model that effectively balances global and local feature processing while avoiding the computational redundancy typical of larger models. PMFSNet streamlines the UNet-based hierarchical structure and simplifies the self-attention mechanism’s computational complexity, making it suitable for lightweight applications. It incorporates a plug-and-play PMFS block, a multi-scale feature enhancement module based on attention mechanisms, to capture long-term dependencies.</div></div><div><h3>Results:</h3><div>The extensive comprehensive results demonstrate that our method achieves superior performance in various segmentation tasks on different data scales even with fewer than a million parameters. Results reveal that our PMFSNet achieves IoU of 84.68%, 82.02%, 78.82%, and 76.48% on public datasets of 3D CBCT Tooth, ovarian tumors ultrasound (MMOTU), skin lesions dermoscopy (ISIC 2018), and gastrointestinal polyp (Kvasir SEG), and yields DSC of 78.29%, 77.45%, and 78.04% on three retinal vessel segmentation datasets, DRIVE, STARE, and CHASE-DB1, respectively.</div></div><div><h3>Conclusion:</h3><div>Our proposed model exhibits competitive performance across various datasets, accomplishing this with significantly fewer model parameters and inference time, demonstrating its value in model integration and deployment. It strikes an optimal compromise between efficiency and performance and can be a highly efficient solution for medical image analysis in resource-constrained clinical environments. The source code is available at <span><span>https://github.com/yykzjh/PMFSNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108611"},"PeriodicalIF":4.9,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074233","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 computationally efficient FEM platform for comprehensive simulations of photoacoustic imaging","authors":"Reza Rahpeima,&nbsp;Chieh-Hsun Wen,&nbsp;Pai-Chi Li","doi":"10.1016/j.cmpb.2025.108620","DOIUrl":"10.1016/j.cmpb.2025.108620","url":null,"abstract":"<div><h3>Background and Objective</h3><div>This study introduces a comprehensive finite element method (FEM) platform to overcome limitations in photoacoustic imaging (PAI) simulations, addressing challenges associated with the simplified numerical methods and rudimentary geometries of existing simulators. The objective is to develop a physics-based numerical simulation method that comprehensively models the entire PAI process, encompassing the various physics processes involved from the initial laser irradiation to the final image reconstruction stage, and producing results that closely replicate real-world scenarios.</div></div><div><h3>Methods</h3><div>The proposed comprehensive simulation platform models the physics of ray optics, bioheat transfer, solid mechanics, elastic waves, and pressure acoustics, encompassing all the various physical processes involved in PAI. This platform employs time-explicit numerical methods, making it computationally efficient and attractive for preclinical analyses. The method was validated by comparing the results of FEM simulations with those from k-wave simulations and experimental tests. The simulations focus on an anatomically realistic breast phantom to demonstrate the induced effects of laser irradiation.</div></div><div><h3>Results</h3><div>The FEM simulation results revealed that laser irradiation caused a slight temperature increase of approximately 0.6 °C in the tumor area. This temperature increase led to the generation of a maximum pressure stress of 853,000 N m^–2 due to thermoelastic expansion, resulting in the production of acoustic waves with a maximum acoustic pressure of 446 kPa after 2 μs of propagation. These acoustic waves propagate, and are detected by a transducer for subsequent image reconstruction. The reported findings highlight the platform's high precision in simulating PAI, including all of its intermediate steps.</div></div><div><h3>Conclusions</h3><div>The developed FEM platform is versatile across diverse scenarios, making it a powerful tool for various applications such as PAI simulations of different body parts, evaluation of various beamforming methods, and consideration of different transducer types. The applications of the platform include temperature monitoring during hyperthermia therapy. This simulation method also has significant potential for training machine-learning and deep-learning models.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108620"},"PeriodicalIF":4.9,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094957","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":"TSPE: Reconstruction of multi-morphological tumors of NIR-II fluorescence molecular tomography based on positional encoding","authors":"Keyi Han ,&nbsp;Chunzhao Li ,&nbsp;Anqi Xiao ,&nbsp;Yaqi Tian ,&nbsp;Jie Tian ,&nbsp;Zhenhua Hu","doi":"10.1016/j.cmpb.2024.108554","DOIUrl":"10.1016/j.cmpb.2024.108554","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Fluorescence molecular tomography (FMT) is a noninvasive and highly sensitive imaging modality, which can display 3D visualization of tumors by reconstructing fluorescence probes’ distribution. However, existing methods mostly ignore positional information, which includes spatial structure information crucial for the reconstruction of light sources. This limits the reconstruction accuracy of light sources with multiple morphologies. Therefore, to our best knowledge, we for the first time integrated positional encoding into the FMT task, enabling the incorporation of high-frequency spatial structure information.</div></div><div><h3>Methods</h3><div>We proposed a three-stage network embedded with a positional encoding module (TSPE) to perform high reconstruction accuracy of tumors with multiple morphologies. Additionally, our study focused on NIR-II which had less severe scattering problems and higher imaging accuracy than NIR-I.</div></div><div><h3>Results</h3><div>The simulation experiments demonstrated that TSPE achieved high reconstruction accuracy in NIR-II FMT, with the barycenter error (BCE) for single-tumor reconstruction reaching 0.18 mm, representing a 14 % reduction compared to other methods. TSPE more accurately distinguished adjacent multi-morphological tumors with a minimal edge-to-edge distance (EED) of 0.3 mm. In vivo experiments also showed that TSPE could achieve more accurate reconstruction of tumors compared with other methods.</div></div><div><h3>Conclusions</h3><div>The proposed method can achieve the best reconstruction performance. It has potential to promote the development of NIR-II FMT and its preclinical application.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108554"},"PeriodicalIF":4.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074260","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":"Towards practical and privacy-preserving CNN inference service for cloud-based medical imaging analysis: A homomorphic encryption-based approach","authors":"Yanan Bai ,&nbsp;Hongbo Zhao ,&nbsp;Xiaoyu Shi ,&nbsp;Lin Chen","doi":"10.1016/j.cmpb.2025.108599","DOIUrl":"10.1016/j.cmpb.2025.108599","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive user data must be transmitted to untrusted cloud servers. Existing privacy-preserving solutions are hindered by significant latency issues, stemming from the computational complexity of inner product operations in convolutional layers and the high communication costs of evaluating nonlinear activation functions. These limitations make current solutions impractical for real-world applications.</div></div><div><h3>Methods:</h3><div>In this paper, we address the challenges in mobile cloud-based medical imaging analysis, where users aim to classify private body-related radiological images using a Convolutional Neural Network (CNN) model hosted on a cloud server while ensuring data privacy for both parties. We propose PPCNN, a practical and privacy-preserving framework for CNN Inference. It introduces a novel mixed protocol that combines a low-expansion homomorphic encryption scheme with the noise-based masking method. Our framework is designed based on three key ideas: (1) optimizing computation costs by shifting unnecessary and expensive homomorphic multiplication operations to the offline phase, (2) introducing a coefficient-aware packing method to enable efficient homomorphic operations during the linear layer of the CNN, and (3) employing data masking techniques for nonlinear operations of the CNN to reduce communication costs.</div></div><div><h3>Results:</h3><div>We implemented PPCNN and evaluated its performance on three real-world radiological image datasets. Experimental results show that PPCNN outperforms state-of-the-art methods in mobile cloud scenarios, achieving superior response times and lower usage costs.</div></div><div><h3>Conclusions:</h3><div>This study introduces an efficient and privacy-preserving framework for cloud-based medical imaging analysis, marking a significant step towards practical, secure, and trustworthy AI-driven healthcare solutions.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"261 ","pages":"Article 108599"},"PeriodicalIF":4.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143058362","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}