Medical image analysis最新文献

{"title":"Test-time generative augmentation for medical image segmentation","authors":"Xiao Ma ,&nbsp;Yuhui Tao ,&nbsp;Zetian Zhang ,&nbsp;Yuhan Zhang ,&nbsp;Xi Wang ,&nbsp;Sheng Zhang ,&nbsp;Zexuan Ji ,&nbsp;Yizhe Zhang ,&nbsp;Qiang Chen ,&nbsp;Guang Yang","doi":"10.1016/j.media.2025.103902","DOIUrl":"10.1016/j.media.2025.103902","url":null,"abstract":"<div><div>Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusions, ambiguous boundaries, and variations in imaging devices. Traditional test-time augmentation (TTA) techniques typically rely on predefined geometric and photometric transformations, limiting their adaptability and effectiveness in complex medical scenarios. In this study, we introduced <strong>T</strong>est-<strong>T</strong>ime <strong>G</strong>enerative <strong>A</strong>ugmentation (<strong>TTGA</strong>), a novel augmentation strategy specifically tailored for medical image segmentation at inference time. Different from conventional augmentation strategies that suffer from excessive randomness or limited flexibility, TTGA leverages a domain-fine-tuned generative model to produce contextually relevant and diverse augmentations tailored to the characteristics of each test image. Built upon diffusion model inversion, a masked null-text inversion method is proposed to enable region-specific augmentations during sampling. Furthermore, a dual denoising pathway is designed to balance precise identity preservation with controlled variability. We demonstrate the efficacy of our TTGA through extensive experiments across three distinct segmentation tasks spanning nine datasets. Our results consistently demonstrate that TTGA not only improves segmentation accuracy (with DSC gains ranging from 0.1 % to 2.3 % over the baseline) but also offers pixel-wise error estimation (with DSC gains ranging from 1.1 % to 29.0 % over the baseline). The source code and demonstration are available at: <span><span>https://github.com/maxiao0234/TTGA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103902"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty estimates in pharmacokinetic modelling of DCE-MRI","authors":"Jonas M. Van Elburg ,&nbsp;Natalia V. Korobova ,&nbsp;Mohammad M. Islam ,&nbsp;Marian A. Troelstra ,&nbsp;Oliver J. Gurney-Champion","doi":"10.1016/j.media.2025.103881","DOIUrl":"10.1016/j.media.2025.103881","url":null,"abstract":"<div><div>Dynamic contrast-enhanced (DCE) MRI is a powerful technique for detecting and characterising various diseases by quantifying tissue perfusion. However, accurate perfusion quantification remains challenging due to noisy data and the complexity of pharmacokinetic modelling. Conventional non-linear least squares (NLLS) fitting often yields noisy parameter maps. Although deep-learning algorithms generate smoother, more visually appealing maps, these may lure clinicians into a false sense of security when the maps are incorrect. Hence, reliable uncertainty estimation is crucial for assessing model performance and ensuring clinical confidence.</div><div>Therefore, we implemented an ensemble of mean-variance estimation (MVE) neural networks to quantify perfusion parameters alongside aleatoric (data-driven) and epistemic (model-driven) uncertainties in DCE-MRI. We compared MVE with NLLS and a physics-informed neural network (PINN), both of which used conventional covariance matrix-based uncertainty estimation.</div><div>Simulations demonstrated that MVE achieved the highest accuracy in perfusion and uncertainty estimates. MVE’s aleatoric uncertainty strongly correlated with true errors, whereas NLLS and PINN tended to overestimate uncertainty. Epistemic uncertainty was significantly higher for the data deviating from what was encountered in training (out-of-distribution) in both MVE and PINN ensembles. In vivo, MVE produced smoother and more reliable uncertainty maps than NLLS and PINN, which exhibited outliers and overestimation. Within a liver region of interest, MVE’s uncertainty estimates matched the standard deviation of the data more closely than NLLS and PINN, making it the most accurate method.</div><div>In conclusion, an MVE enhances quantitative DCE-MRI by providing robust uncertainty estimates alongside perfusion parameters. This approach improves the reliability of AI-driven MRI analysis, supporting clinical translation.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103881"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BIASNet: A bidirectional feature alignment and semantics-guided network for weakly-supervised medical image registration","authors":"Housheng Xie,&nbsp;Xiaoru Gao,&nbsp;Guoyan Zheng","doi":"10.1016/j.media.2025.103913","DOIUrl":"10.1016/j.media.2025.103913","url":null,"abstract":"<div><div>Medical image registration, which establishes spatial correspondences between different medical images, serves as a fundamental process in numerous clinical applications and diagnostic workflows. Despite significant advancement in unsupervised deep learning-based registration methods, these approaches consistently yield suboptimal results compared to their weakly-supervised counterparts. Recent advancements in universal segmentation models have made it easier to obtain anatomical labels from medical images. However, existing registration methods have not fully leveraged the rich anatomical and structural prior information provided by segmentation labels. To address this limitation, we propose a <strong>BI</strong>directional feature <strong>A</strong>lignment and <strong>S</strong>emantics-guided Network, referred to as BIASNet, for weakly-supervised image registration. Specifically, starting from multi-scale features extracted from the pre-trained VoCo, fine-tuned using Low-Rank Adaptation (LoRA), we propose a dual-attribute learning scheme, incorporating a novel BIdirectional Alignment and Fusion (BIAF) module for extracting both semantics-wise and intensity-wise features. These two types of features are subsequently fed into a semantics-guided progressive registration framework for accurate deformation field estimation. We further propose an anatomical region deformation consistency learning to regularize the target anatomical regions deformation. Comprehensive experiments conducted on three typical yet challenging datasets demonstrate that our method achieves consistently better results than other state-of-the-art deformable registration approaches. The source code is publicly available at <span><span>https://github.com/xiehousheng/BIASNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103913"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145730671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GloW-VSNet: A scribble-based weakly supervised framework for global-view vitiligo lesion segmentation","authors":"Yuheng Wang ,&nbsp;Yuhan Zheng ,&nbsp;Chloe Yue ,&nbsp;Thomas Zhang ,&nbsp;Jiayue Cai ,&nbsp;Chunqi Chang ,&nbsp;Harvey Lui ,&nbsp;Sunil Kalia ,&nbsp;Z. Jane Wang ,&nbsp;Tim K. Lee","doi":"10.1016/j.media.2025.103920","DOIUrl":"10.1016/j.media.2025.103920","url":null,"abstract":"<div><div>Vitiligo lesion identification is essential for quantifying disease severity, monitoring disease progression and assessing treatment response, particularly for objective quantification. However, segmenting vitiligo lesions from clinical images is challenging due to indistinct borders, complex backgrounds, and image artifacts. The difficulty increases when handling small and sparse lesions in global-view photographs. Fully supervised segmentation models require extensively annotated datasets, making the labelling process time-consuming and costly. To address these challenges, we propose GloW-VSNet, a scribble-guided weakly supervised segmentation method for global-view vitiligo detection. Our approach integrates differentiable feature clustering with a spatial attention mechanism based on physician-provided scribble annotations, enabling the model to focus on relevant spatial features and improve segmentation accuracy despite background noise and artifacts. Additionally, we introduce spatial continuity optimization to preserve the natural distribution of vitiligo, enhancing segmentation consistency while reducing computational demands. Extensive experiments on two public vitiligo datasets and two private datasets demonstrate that GloW-VSNet achieves state-of-the-art performance. To our knowledge, this is the first study to explore weakly supervised global-view vitiligo segmentation, addressing a critical research gap. Our method enhances the assessment of disease severity and monitoring of treatment response through an objective assessment for real-world applications. Our code is publicly available at <span><span>https://github.com/YuhanZheng0327/Weakly-Supervised-Vitiligo-Lesion-Segmentation</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103920"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic text-guided representation learning in hierarchical classification for pathological whole slide image","authors":"Jiawen Li ,&nbsp;Qiehe Sun ,&nbsp;Renao Yan ,&nbsp;Yizhi Wang ,&nbsp;Yuqiu Fu ,&nbsp;Yani Wei ,&nbsp;Tian Guan ,&nbsp;Huijuan Shi ,&nbsp;Yonghong He ,&nbsp;Anjia Han","doi":"10.1016/j.media.2025.103894","DOIUrl":"10.1016/j.media.2025.103894","url":null,"abstract":"<div><div>With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer diagnosis. Limited by the expensive cost of pixel-level annotation, current research primarily focuses on representation learning with slide-level labels, showing success in various downstream tasks. However, given the diversity of lesion types and the complex relationships between each other, these techniques still deserve further exploration in addressing advanced pathology tasks. To this end, we introduce the concept of hierarchical pathological image classification and propose a representation learning called PathTree. PathTree considers the multi-classification of diseases as a binary tree structure. Each category is represented as a professional pathological text description, which messages information with a tree-like encoder. The interactive text features are then used to guide the aggregation of hierarchical multiple representations. PathTree uses slide-text similarity to obtain probability scores and introduces two extra tree-specific losses to further constrain the association between texts and slides. Through extensive experiments on three challenging hierarchical classification datasets: in-house cryosectioned lung tissue lesion identification, public prostate cancer grade assessment, and public breast cancer subtyping, our proposed PathTree is consistently competitive compared to the state-of-the-art methods and provides a new perspective on the deep learning-assisted solution for more complex WSI classification.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103894"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable classification of endomicroscopic brain data via saliency consistent contrastive learning","authors":"Chi Xu ,&nbsp;Alfie Roddan ,&nbsp;Irini Kakaletri ,&nbsp;Patra Charalampaki ,&nbsp;Stamatia Giannarou","doi":"10.1016/j.media.2025.103917","DOIUrl":"10.1016/j.media.2025.103917","url":null,"abstract":"<div><div>In neurosurgery, accurate brain tissue characterization via probe-based Confocal Laser Endomicroscopy (pCLE) has become popular for guiding surgical decisions and ensuring safe tumour resections. In order to enable surgeons to trust a tissue classification model, interpretability of the result is required. However, state-of-the-art (SOTA) deep learning models for pCLE data classification exhibit limited interpretability. This paper introduces a novel image classification framework for interpretable brain tissue characterisation using pCLE data. Firstly, instead of the commonly employed cross-entropy based classification loss, we propose Label Contrastive Learning (LCL) loss to learn intra-category similarities and inter-category contrasts. We are then able to generate highly representative data embeddings, which not only improve classification performance but also distinguish characteristics from different tissue classes. Secondly, we design a Saliency Consistency (SC) module to enable the trained model to generate clinically relevant saliency maps of the input data. To further refine the saliency maps, a novel Top-K Maximum and Minimum Pooling (TK-MMP) layer is introduced to our SC module, to increase the contrast of saliency values between non-clinically relevant and clinically relevant areas. For the first time, the Exponential Moving Average (EMA) is used in a novel fashion to update global embeddings of the different tissue categories rather than the weights of the model. In addition, we propose a Global Embedding Inference (GEI) layer to replace learnable classification layers to achieve more robust classification by estimating the cosine similarity between the input data embeddings and global embeddings. Performance evaluation on <em>ex-vivo</em> and <em>in-vivo</em> pCLE brain data verifies that our proposed approach outperforms SOTA classification models in terms of accuracy, robustness and interpretability. Our source codes are released at: <span><span>https://github.com/XC9292/LCL-SC.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103917"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perivascular space identification nnUNet for generalised usage (PINGU)","authors":"Benjamin Sinclair ,&nbsp;William Pham ,&nbsp;Lucy Vivash ,&nbsp;Jasmine Moses ,&nbsp;Miranda Lynch ,&nbsp;Karina Dorfman ,&nbsp;Cassandra Marotta ,&nbsp;Shaun Koh ,&nbsp;Jacob Bunyamin ,&nbsp;Ella Rowsthorn ,&nbsp;Alex Jarema ,&nbsp;Himashi Peiris ,&nbsp;Zhaolin Chen ,&nbsp;Sandy R Shultz ,&nbsp;David K Wright ,&nbsp;Dexiao Kong ,&nbsp;Sharon L. Naismith ,&nbsp;Terence J. O’Brien ,&nbsp;Meng Law","doi":"10.1016/j.media.2025.103903","DOIUrl":"10.1016/j.media.2025.103903","url":null,"abstract":"<div><div>Perivascular spaces (PVSs) form a central component of the brain’s waste clearance system, the glymphatic system. These structures are visible on MRIs when enlarged, and their morphology is associated with aging and neurological disease. Manual quantification of PVS is time consuming and subjective. Numerous deep learning methods for PVS segmentation have been developed for automated segmentation. However, the majority of these algorithms have been developed and evaluated on homogenous datasets and high resolution scans, perhaps limiting their applicability for the wide range of image qualities acquired in clinical and research settings. In this work we train a nnUNet, a top-performing task driven biomedical image segmentation deep learning algorithm, on a heterogenous training sample of manually segmented MRIs of a range of different qualities and resolutions from 7 different datasets acquired on 6 different scanners. These are compared to the two currently publicly available deep learning methods for 3D segmentation of PVS, evaluated on scans with a range of resolutions and qualities. The resulting model, PINGU (Perivascular space Identification Nnunet for Generalised Usage), achieved voxel and cluster level dice scores of 0.50(SD=0.15) and 0.63(0.17) in the white matter (WM), and 0.54 (0.11) and 0.66(0.17) in the basal ganglia (BG). Performance on unseen “external” sites’ data was substantially lower for both PINGU (0.20-0.38 [WM, voxel], 0.29-0.58 [WM, cluster], 0.22-0.36 [BG, voxel], 0.46-0.60 [BG, cluster]) and the publicly available algorithms (0.18-0.30 [WM, voxel], 0.29-0.38 [WM cluster], 0.10-0.20 [BG, voxel], 0.15-0.37 [BG, cluster]). Nonetheless, PINGU strongly outperformed the publicly available algorithms, particularly in the BG. PINGU stands out as broad-use PVS segmentation tool, with particular strength in the BG, an area of PVS highly related to vascular disease and pathology.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103903"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-cancer framework with cancer-aware attention and adversarial mutual-information minimization for whole slide image classification","authors":"Sharon Peled ,&nbsp;Yosef E. Maruvka ,&nbsp;Moti Freiman","doi":"10.1016/j.media.2025.103927","DOIUrl":"10.1016/j.media.2025.103927","url":null,"abstract":"<div><div>Whole Slide Images (WSIs) are crucial in modern pathology, offering high-resolution data for accurate diagnosis, treatment planning, and research. Deep learning methods have recently been proposed to harness this data by extracting and interpreting complex patterns. However, these approaches often focus on specific tumor types, limiting their generalizability across diverse pathological conditions and restricting scalability. This relatively narrow focus ultimately stems from the inherent heterogeneity in histopathology and the diverse morphological and molecular characteristics of different tumors. To this end, we propose a novel approach for multi-cancer WSI analysis, designed to leverage the diversity of different tumor types. We introduce a Cancer-Aware Attention module that models both shared patterns across cancers and cancer-specific variations to address heterogeneity and enhance cross-tumor generalization. Furthermore, we construct an adversarial cancer regularization mechanism to minimize cancer-specific biases through mutual information minimization. Additionally, we develop a hierarchical sample balancing strategy to mitigate data imbalances and promote unbiased learning. Together, these form a cohesive framework for unbiased multi-cancer WSI analysis. Extensive experiments on a uniquely constructed multi-cancer dataset demonstrate significant improvements in generalization, providing a scalable solution for WSI classification across diverse cancer types.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103927"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-contrast CT esophageal varices grading through clinical prior-enhanced multi-organ analysis","authors":"Xiaoming Zhang ,&nbsp;Chunli Li ,&nbsp;Jiacheng Hao ,&nbsp;Yuan Gao ,&nbsp;Danyang Tu ,&nbsp;Jianyi Qiao ,&nbsp;Xiaoli Yin ,&nbsp;Le Lu ,&nbsp;Ling Zhang ,&nbsp;Ke Yan ,&nbsp;Yang Hou ,&nbsp;Yu Shi","doi":"10.1016/j.media.2025.103924","DOIUrl":"10.1016/j.media.2025.103924","url":null,"abstract":"<div><div>Esophageal varices (EV) represent a critical complication of portal hypertension, affecting approximately 60% of cirrhosis patients with a significant bleeding risk of  ∼ 30%. While traditionally diagnosed through invasive endoscopy, non-contrast computed tomography (NCCT) presents a potential non-invasive alternative that has yet to be fully utilized in clinical practice. We present Multi-Organ-COhesion Network++ (MOON++), a novel multimodal framework that enhances EV assessment through comprehensive analysis of NCCT scans. Inspired by clinical evidence correlating organ volumetric relationships with liver disease severity, MOON++ synthesizes imaging characteristics of the esophagus, liver, and spleen through multimodal learning. We evaluated our approach using 1631 patients, those with endoscopically confirmed EV were classified into four severity grades. Validation in 239 patient cases and independent testing in 289 cases demonstrate superior performance compared to conventional single organ methods, achieving an AUC of 0.894 versus 0.803 for the severe grade EV classification (G3 versus  &lt; G3) and 0.921 versus 0.793 for the differentiation of moderate to severe grades ( ≥ G2 versus  &lt; G2). We conducted a reader study involving experienced radiologists to further validate the performance of MOON++. To our knowledge, MOON++ represents the first comprehensive multi-organ NCCT analysis framework incorporating clinical knowledge priors for EV assessment, potentially offering a promising non-invasive diagnostic alternative. Code is available at <span><span>https://github.com/StevenHaojc/MOON</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103924"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLIP-Guided Generative network for pathology nuclei image augmentation","authors":"Yanan Zhang ,&nbsp;Qingyang Liu ,&nbsp;Qian Chen ,&nbsp;Xiangzhi Bai","doi":"10.1016/j.media.2025.103908","DOIUrl":"10.1016/j.media.2025.103908","url":null,"abstract":"<div><div>Nuclei segmentation and classification play a crucial role in the quantitative analysis of computational pathology (CPath). However, the challenge of creating a large volume of labeled pathology nuclei images due to annotation costs has significantly limited the performance of deep learning-based nuclei segmentation methods. Generative data augmentation offers a promising solution by substantially expanding the available training data without additional annotations. In medical image analysis, Generative Adversarial Networks (GANs) were effective for data augmentation, enhancing model performance by generating realistic synthetic data. However, these approaches lack scalability for multi-class data, as nuclei masks cannot provide sufficient information for diverse image generation. Recently, visual-language foundation models, pretrained on large-scale image-caption pairs, have demonstrated robust performance in pathological diagnostic tasks. In this study, we propose a CLIP-guided generative data augmentation method for nuclei segmentation and classification, leveraging the pretrained pathological CLIP text and image encoders in both the generator and discriminator. Specifically, we first create text descriptions by processing paired histopathology images and nuclei masks, which include information such as organ tissue type, cell count, and nuclei types. These paired text descriptions and nuclei masks are then fed into our multi-modal conditional image generator to guide the synthesis of realistic histopathology images. To ensure the quality of synthesized images, we utilize a high-resolution image discriminator and a CLIP image encoder-based discriminator, focusing on both local and global features of histopathology images. The synthetic histopathology images, paired with corresponding nuclei masks, are integrated into the real dataset to train the nuclei segmentation and classification model. Our experiments, conducted on diverse publicly available pathology nuclei datasets, including both qualitative and quantitative analysis, demonstrate the effectiveness of our proposed method. The experimental results of the nuclei segmentation and classification task underscore the advantages of our data augmentation approach. The code is available at <span><span>https://github.com/zhangyn1415/CGPN-GAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"109 ","pages":"Article 103908"},"PeriodicalIF":11.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}