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Corrigendum to “Fourier Convolution Block with global receptive field for MRI reconstruction” [Medical Image Analysis (2025) Volume 99, 103349] Med. Image Anal. (IF 11.8) Pub Date : 2025-06-21 Haozhong Sun, Yuze Li, Zhongsen Li, Runyu Yang, Ziming Xu, Jiaqi Dou, Haikun Qi, Huijun Chen
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Domain agnostic 2D-3D deformable registration Application to fluoroscopic guidance without contrast agent Med. Image Anal. (IF 11.8) Pub Date : 2025-06-21 François Lecomte, Juan Verde, Jean-Louis Dillenseger, Stéphane Cotin
We present a method for estimating, in real time, a 3D displacement field from a single fluoroscopic image. Our approach uses a fully convolutional network architecture to solve the associated inverse problem. Supervised learning is performed on synthetic data, using Digitally Reconstructed Radiographs as input and displacement fields as output. We use randomized Gaussian kernels to produce a synthetic
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Shadow defense against gradient inversion attack in federated learning Med. Image Anal. (IF 11.8) Pub Date : 2025-06-21 Le Jiang, Liyan Ma, Guang Yang
Federated learning (FL) has emerged as a transformative framework for privacy-preserving distributed training, allowing clients to collaboratively train a global model without sharing their local data. This is especially crucial in sensitive fields like healthcare, where protecting patient data is paramount. However, privacy leakage remains a critical challenge, as the communication of model updates
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MetaExplainer: Revisit domain generalization of functional connectome analyses from the perspective of explainability Med. Image Anal. (IF 11.8) Pub Date : 2025-06-21 Xinmei Qiu, Yongheng Sun, Yilin Shi, Xujun Duan, Fan Wang, Jianhua Ma
Graph neural networks (GNNs) are at the forefront of learning-based functional connectome analyses and neuropsychiatric disorder diagnoses with fMRI data. The reliability of the deployed GNNs largely depends on their explainability and generalizability, due to the complexity of functional brain networks and heterogeneous fMRI data acquisitions. While developing explainable or generalizable GNNs has
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Patient-Specific CBCT Synthesis for Ultra-fast Tumor Localization in Surface-guided Radiotherapy Med. Image Anal. (IF 11.8) Pub Date : 2025-06-20 Shaoyan Pan, Vanessa Su, Junbo Peng, Junyuan Li, Yuan Gao, Chih-Wei Chang, Tonghe Wang, Zhen Tian, Xiaofeng Yang
We propose an Advanced Surface Imaging (A-SI) framework to enable ultra-fast tumor localization for real-time tumor motion tracking in surface-guided radiotherapy (SGRT). SGRT uses optical surface imaging (OSI) to acquire ultra-fast surface topography images of the patient on the treatment couch. This serves as a surrogate for intra-fractional tumor motion tracking to guide radiation delivery. However
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Prior Anatomical Knowledge-guided GAN for ICL surgery postoperative prediction based on AS-OCT image Med. Image Anal. (IF 11.8) Pub Date : 2025-06-20 Yinglin Zhang, Ruiling Xi, Risa Higashita, Keiichiro Okamoto, Kazutaka Kamiya, Kazunori Miyata, Akihito Igarashi, Seiichiro Hata, Tomoaki Nakamura, Jiang Liu
Accurate prediction of postoperative vault, the distance between the implantable collamer lens (ICL) posterior surface and the crystalline lens anterior surface, is critical for the success of ICL surgery. Existing regression-based prediction methods fail to provide visual postoperative observations, which are essential for a comprehensive risk assessment. Anterior segment optical coherence tomography
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When evidence modeling meets knowledge distillation: Towards reliable contrast-enhanced knowledge distillation for non-contrast medical image segmentation Med. Image Anal. (IF 11.8) Pub Date : 2025-06-19 Jianfeng Zhao, Shuo Li
Contrast-enhanced knowledge distillation promises to transform medical diagnostics and reveal promising approaches for tumor segmentation on non-contrast medical images. However, existing methods related to contrast-enhanced knowledge distillation still make it hard to distill reliable contrast-enhanced knowledge for tumor segmentation due to the limitations of (1) unable to quantify uncertainty information
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Corrigendum to “Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework” [Medical Imaging Analysis 90 (2023) 102960] Med. Image Anal. (IF 11.8) Pub Date : 2025-06-17 Zong Fan, Ping Gong, Shanshan Tang, Christine U. Lee, Xiaohui Zhang, Pengfei Song, Shigao Chen, Hua Li
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Synthesizing individualized aging brains in health and disease with generative models and parallel transport Med. Image Anal. (IF 11.8) Pub Date : 2025-06-16 Jingru Fu, Yuqi Zheng, Neel Dey, Daniel Ferreira, Rodrigo Moreno
Simulating prospective magnetic resonance imaging (MRI) scans from a given individual brain image is challenging, as it requires accounting for canonical changes in aging and/or disease progression while also considering the individual brain’s current status and unique characteristics. While current deep generative models can produce high-resolution anatomically accurate templates for population-wide
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TCFNet: Bidirectional face-bone transformation via a Transformer-based coarse-to-fine point movement network Med. Image Anal. (IF 11.8) Pub Date : 2025-06-16 Runshi Zhang, Bimeng Jie, Yang He, Junchen Wang
Computer-aided surgical simulation is a critical component of orthognathic surgical planning, where accurately simulating face-bone shape transformations is significant. The traditional biomechanical simulation methods are limited by their computational time consumption levels, labor-intensive data processing strategies and low accuracy. Recently, deep learning-based simulation methods have been proposed
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LMS-Net: A learned Mumford-Shah network for binary few-shot medical image segmentation Med. Image Anal. (IF 11.8) Pub Date : 2025-06-15 Shengdong Zhang, Fan Jia, Xiang Li, Hao Zhang, Jun Shi, Liyan Ma, Shihui Ying
Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient interpretability and fail to fully incorporate the underlying physical structures of semantic regions. To address these issues, in this paper, we propose a novel deep unfolding network, called
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One-shot cell segmentation via learning memory query: Towards universal solution without active tuning Med. Image Anal. (IF 10.7) Pub Date : 2025-06-15 Jintu Zheng, Qizhe Liu, Yi Ding, Yi Cao, Ying Hu, Zenan Wang
Cell segmentation, which involves separating individual cells in biomedical images, is essential for disease analysis and drug development research. However, many existing methods are restricted to specific types of images or require constant adjustment, making them time-consuming and labor-intensive. We introduce a new framework called Mimic, which employs a ”Query-and-Answer” (Q&A) mechanism to segment
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SurgRIPE challenge: Benchmark of surgical robot instrument pose estimation Med. Image Anal. (IF 11.8) Pub Date : 2025-06-14 Haozheng Xu, Alistair Weld, Chi Xu, Alfie Roddan, João Cartucho, Mert Asim Karaoglu, Alexander Ladikos, Yangke Li, Yiping Li, Daiyun Shen, Geonhee Lee, Seyeon Park, Jongho Shin, Lucy Fothergill, Dominic Jones, Pietro Valdastri, Duygu Sarikaya, Stamatia Giannarou
Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods for surgical instrument pose estimation provide a practical approach to tool tracking, but they often require markers to be attached to the instruments. Recently, more research has focused on the development of markerless
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DeepSPV: A deep learning pipeline for 3D spleen volume estimation from 2D ultrasound images Med. Image Anal. (IF 11.8) Pub Date : 2025-06-10 Zhen Yuan, David Stojanovski, Lei Li, Alberto Gomez, Haran Jogeesvaran, Esther Puyol-Antón, Baba Inusa, Andrew P. King
Splenomegaly, the enlargement of the spleen, is an important clinical indicator for various associated medical conditions, such as sickle cell disease (SCD). Spleen length measured from 2D ultrasound is the most widely used metric for characterising spleen size. However, it is still considered a surrogate measure, and spleen volume remains the gold standard for assessing spleen size. Accurate spleen
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ACOUSLIC-AI challenge report: Fetal abdominal circumference measurement on blind-sweep ultrasound data from low-income countries Med. Image Anal. (IF 11.8) Pub Date : 2025-06-10 M. Sofia Sappia, Chris L. de Korte, Bram van Ginneken, Dean Ninalga, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Tanya Akumu, Carlos Martín-Isla, Karim Lekadir, Victor M. Campello, Jorge Fabila, Anette Beverdam, Jeroen van Dillen, Chase Neff, Keelin Murphy
Fetal growth restriction, affecting up to 10% of pregnancies, is a critical factor contributing to perinatal mortality and morbidity. Ultrasound measurements of the fetal abdominal circumference (AC) are a key aspect of monitoring fetal growth. However, the routine practice of biometric obstetric ultrasounds is limited in low-resource settings due to the high cost of sonography equipment and the scarcity
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Robust image representations with counterfactual contrastive learning Med. Image Anal. (IF 10.7) Pub Date : 2025-06-10 Mélanie Roschewitz, Fabio De Sousa Ribeiro, Tian Xia, Galvin Khara, Ben Glocker
Contrastive pretraining can substantially increase model generalisation and downstream performance. However, the quality of the learned representations is highly dependent on the data augmentation strategy applied to generate positive pairs. Positive contrastive pairs should preserve semantic meaning while discarding unwanted variations related to the data acquisition domain. Traditional contrastive
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Evaluation of techniques for automated classification and artery quantification of the circle of Willis on TOF-MRA images: The CROWN challenge Med. Image Anal. (IF 10.7) Pub Date : 2025-06-07 Iris N. Vos, Ynte M. Ruigrok, Edwin Bennink, Mireille R.E. Velthuis, Barbara Paic, Maud E.H. Ophelders, Myrthe A.D. Buser, Bas H.M. van der Velden, Chen Geng, Matthieu Coupet, Félix Dumais, Adrian Galdran, Junyi Zhang, Wei Liu, Ting Ma, Madhu S. Nair, Mathieu Naudin, Preena K.P., Keerthi A.S. Pillai, Pengcheng Shi, Thierry Urruty, Yakang Dai, Kaiyuan Yang, Fabio Musio, Bjoern H. Menze, Birgitta K.
Assessing risk factors for intracranial aneurysm (IA) development on images is crucial for early detection of high-risk cases. IAs often form at bifurcations within the circle of Willis (CoW), but manual assessment of these arteries is both time-consuming and susceptible to inconsistencies. Previous studies on imaging markers for IA development lack sufficient evidence for clinical implications, highlighting
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Causal inertia proximal Mamba network for magnetic resonance image reconstruction Med. Image Anal. (IF 10.7) Pub Date : 2025-06-07 Tong Hou, Hongqing Zhu, Bingcang Huang, Kai Chen, Zhong Zheng
Accurate and rapid Magnetic Resonance Imaging (MRI) is critical for clinical diagnosis. However, different sampling strategies and datasets act as confounding factors, significantly impacting the quality of image reconstruction. While existing methods can capture correlations between data during the imaging process, they overlook the deeper associations rooted in causal relationships. To address this
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End-to-end breast cancer radiotherapy planning via LMMs with consistency embedding Med. Image Anal. (IF 10.7) Pub Date : 2025-06-06 Kwanyoung Kim, Yujin Oh, Sangjoon Park, Hwa Kyung Byun, Joongyo Lee, Jin Sung Kim, Yong Bae Kim, Jong Chul Ye
Recent advances in AI foundation models have significant potential for lightening the clinical workload by mimicking the comprehensive and multi-faceted approaches used by medical professionals. In the field of radiation oncology, the integration of multiple modalities holds great importance, so the opportunity of foundational model is abundant. Inspired by this, here we present RO-LMM, a multi-purpose
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A novel explainable AI framework for medical image classification integrating statistical, visual, and rule-based methods Med. Image Anal. (IF 10.7) Pub Date : 2025-06-06 Naeem Ullah, Florentina Guzmán-Aroca, Francisco Martínez-Álvarez, Ivanoe De Falco, Giovanna Sannino
Artificial intelligence and deep learning are powerful tools for extracting knowledge from large datasets, particularly in healthcare. However, their black-box nature raises interpretability concerns, especially in high-stakes applications. Existing eXplainable Artificial Intelligence methods often focus solely on visualization or rule-based explanations, limiting interpretability’s depth and clarity
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Learning multi-modal representations by watching hundreds of surgical video lectures Med. Image Anal. (IF 10.7) Pub Date : 2025-06-04 Kun Yuan, Vinkle Srivastav, Tong Yu, Joël L. Lavanchy, Jacques Marescaux, Pietro Mascagni, Nassir Navab, Nicolas Padoy
Recent advancements in surgical computer vision applications have been driven by vision-only models, which do not explicitly integrate the rich semantics of language into their design. These methods rely on manually annotated surgical videos to predict a fixed set of object categories, limiting their generalizability to unseen surgical procedures and downstream tasks. In this work, we put forward the
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Multi-instance curriculum learning for histopathology image classification with bias reduction Med. Image Anal. (IF 10.7) Pub Date : 2025-06-03 Zihao Mi, Jianan Zhang, Xueyu Liu, Guanghui Yue, Junhong Yue, Mingqiang Wei, Yidi Li, Yongfei Wu
Multi-instance learning (MIL) exhibits advanced and surpassed capabilities in understanding and recognizing complex patterns within gigapixel histopathological images. However, current MIL methods for the analysis of the histopathological images still give rise to two main concerns. On one hand, vanilla MIL methods intuitively focus on identifying salient instances (easy-to-classify instances) without
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Accurate and efficient cardiac digital twin from surface ECGs: Insights into identifiability of ventricular conduction system Med. Image Anal. (IF 10.7) Pub Date : 2025-06-03 Thomas Grandits, Karli Gillette, Gernot Plank, Simone Pezzuto
Digital twins for cardiac electrophysiology are an enabling technology for precision cardiology. Current forward models are advanced enough to simulate the cardiac electric activity under different pathophysiological conditions and accurately replicate clinical signals like torso electrocardiograms (ECGs). In this work, we address the challenge of matching subject-specific QRS complexes using anatomically
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ElastoNet: Neural network-based multicomponent MR elastography wave inversion with uncertainty quantification Med. Image Anal. (IF 10.7) Pub Date : 2025-06-01 Héloïse Bustin, Tom Meyer, Rolf Reiter, Jakob Jordan, Lars Walczak, Heiko Tzschätzsch, Ingolf Sack, Anja Hennemuth
Magnetic Resonance Elastography (MRE) quantifies soft tissue stiffness by measuring induced shear waves. MRE inversion techniques for parameter reconstruction are often affected by noise and compression waves. Neural network-based inversions have emerged as a possible solution to address these challenges. However, current approaches lack generalizability and do not provide uncertainty estimates. Therefore
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Similarity-guided multi-view functional brain network fusion Med. Image Anal. (IF 10.7) Pub Date : 2025-05-31 Zhigang Li, Jingyu Liu, Mengkai Sun, Fa Zhang, Bin Hu, Qunxi Dong
Understanding the intricate patterns and interactions within functional brain networks (FBNs) is crucial for the accurate diagnosis and analysis of mental disorders. Brain function can be represented through different brain networks, each providing complementary insights into underlying neural processes. Integrating data from these different sources enables a more comprehensive and precise understanding
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Automated labeling using tracked ultrasound imaging: Application in tracking vertebrae during spine surgery Med. Image Anal. (IF 10.7) Pub Date : 2025-05-31 Debarghya China, Luke J. MacLean, Jinchi Wei, Nicholas Theodore, Norbert Johnson, Neil Crawford, Kai Ding, Ali Uneri
Recent advancements in machine learning (ML) allow for rapid analysis of complex image data, which supports the use of ultrasound (US)-based solutions in interventional procedures. These solutions often require large, labeled datasets that can be time-consuming to curate and subject to inter- and intra-labeler variability. This work presents a practical method for automated labeling of US images by
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Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications Med. Image Anal. (IF 10.7) Pub Date : 2025-05-30 Daan Schouten, Giulia Nicoletti, Bas Dille, Catherine Chia, Pierpaolo Vendittelli, Megan Schuurmans, Geert Litjens, Nadieh Khalili
Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While artificial intelligence (AI) models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance clinical decision-making. This scoping review
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Corrigendum to “PSFHS challenge report: pubic symphysis and fetal head segmentation from intrapartum ultrasound images” [Medical Image Analysis 99 (2025),103353] Med. Image Anal. (IF 10.7) Pub Date : 2025-05-29 Jieyun Bai, Zihao Zhou, Zhanhong Ou, Gregor Koehler, Raphael Stock, Klaus Maier-Hein, Marawan Elbatel, Robert Martí, Xiaomeng Li, Yaoyang Qiu, Panjie Gou, Gongping Chen, Lei Zhao, Jianxun Zhang, Yu Dai, Fangyijie Wang, Guénolé Silvestre, Kathleen Curran, Hongkun Sun, Jing Xu, Pengzhou Cai, Lu Jiang, Libin Lan, Dong Ni, Mei Zhong, Gaowen Chen, Víctor M. Campello, Yaosheng Lu, Karim Lekadir
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Deep learning detection of acute and sub-acute lesion activity from single-timepoint conventional brain MRI in multiple sclerosis Med. Image Anal. (IF 10.7) Pub Date : 2025-05-28 Quentin Spinat, Benoit Audelan, Xiaotong Jiang, Bastien Caba, Alexis Benichoux, Despoina Ioannidou, Olivier Teboul, Nikos Komodakis, Willem Huijbers, Refaat Gabr, Arie Gafson, Colm Elliott, Douglas Arnold, Nikos Paragios, Shibeshih Belachew
Multiple sclerosis (MS) is a chronic inflammatory disease characterized by demyelinating lesions in the central nervous system. Cross-sectional measurements of acute inflammatory lesion activity are typically obtained by detecting the presence of gadolinium enhancement in lesions, which typically lasts 3-6 weeks. We formulate the novel and clinically relevant task of quantification of recent acute
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Mitigating medical dataset bias by learning adaptive agreement from a biased council Med. Image Anal. (IF 10.7) Pub Date : 2025-05-28 Luyang Luo, Xin Huang, Minghao Wang, Zhuoyue Wan, Wanteng Ma, Hao Chen
Dataset bias in images is an important yet less explored topic in medical images. Deep learning could be prone to learning spurious correlation raised by dataset bias, resulting in inaccurate, unreliable, and unfair models, which impedes its adoption in real-world clinical applications. Despite its significance, there is a dearth of research in the medical image classification domain to address dataset
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An orchestration learning framework for ultrasound imaging: Prompt-Guided Hyper-Perception and Attention-Matching Downstream Synchronization Med. Image Anal. (IF 10.7) Pub Date : 2025-05-27 Zehui Lin, Shuo Li, Shanshan Wang, Zhifan Gao, Yue Sun, Chan-Tong Lam, Xindi Hu, Xin Yang, Dong Ni, Tao Tan
Ultrasound imaging is pivotal in clinical diagnostics due to its affordability, portability, safety, real-time capability, and non-invasive nature. It is widely utilized for examining various organs, such as the breast, thyroid, ovary, cardiac, and more. However, the manual interpretation and annotation of ultrasound images are time-consuming and prone to variability among physicians. While single-task
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Corrigendum to “LESS: Label-efficient multi-scale learning for cytological whole slide image screening” [Medical Image Analysis 94 (2024): 103109] Med. Image Anal. (IF 10.7) Pub Date : 2025-05-27 Beidi Zhao, Wenlong Deng, Zi-Han Li, Chen Zhou, Zuhua Gao, Gang Wang, Xiaoxiao Li
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Efficient few-shot medical image segmentation via self-supervised variational autoencoder Med. Image Anal. (IF 10.7) Pub Date : 2025-05-26 Yanjie Zhou, Feng Zhou, Fengjun Xi, Yong Liu, Yun Peng, David E. Carlson, Liyun Tu
Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, inevitable spatial misalignments during registration can lead to inaccuracies and diminished segmentation quality. To address this, we developed EFS-MedSeg
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ScanAhead: Simplifying standard plane acquisition of fetal head ultrasound Med. Image Anal. (IF 10.7) Pub Date : 2025-05-26 Qianhui Men, He Zhao, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble
The fetal standard plane acquisition task aims to detect an Ultrasound (US) image characterized by specified anatomical landmarks and appearance for assessing fetal growth. However, in practice, due to variability in human operator skill and possible fetal motion, it can be challenging for a human operator to acquire a satisfactory standard plane. To support a human operator with this task, this paper
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HGMSurvNet: A two-stage hypergraph learning network for multimodal cancer survival prediction Med. Image Anal. (IF 10.7) Pub Date : 2025-05-23 Saisai Ding, Linjin Li, Ge Jin, Jun Wang, Shihui Ying, Jun Shi
Cancer survival prediction based on multimodal data (e.g., pathological slides, clinical records, and genomic profiles) has become increasingly prevalent in recent years. A key challenge of this task is obtaining an effective survival-specific global representation from patient data with highly complicated correlations. Furthermore, the absence of certain modalities is a common issue in clinical practice
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Learnable prototype-guided multiple instance learning for detecting tertiary lymphoid structures in multi-cancer whole-slide pathological images Med. Image Anal. (IF 10.7) Pub Date : 2025-05-23 Pengfei Xia, Dehua Chen, Huimin An, Kiat Shenq Lim, Xiaoqun Yang
Tertiary lymphoid structures (TLS) are ectopic lymphoid aggregates that form under specific pathological conditions, such as chronic inflammation and malignancies. Their presence within the tumor microenvironment (TME) is strongly correlated with patient prognosis and response to immunotherapy, making TLS detection in whole-slide pathological images (WSIs) crucial for clinical decision-making. Although
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MSFusion: A multi-source hybrid feature fusion network for accurate grading of invasive breast cancer using H&E-stained histopathological images Med. Image Anal. (IF 10.7) Pub Date : 2025-05-23 Yuli Chen, Jiayang Bai, Jinjie Wang, Guoping Chen, Xinxin Zhang, Duan-Bo Shi, Xiujuan Lei, Peng Gao, Cheng Lu
Invasive breast cancer (IBC) is a prevalent malignant tumor in women, and precise grading plays a pivotal role in ensuring effective treatment and enhancing survival rates. However, accurately grading IBC presents a significant challenge due to its heterogeneous nature and the need to harness the complementary information from multiple nuclei sources in histopathology images. To tackle this critical
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Learning contrast and content representations for synthesizing magnetic resonance image of arbitrary contrast Med. Image Anal. (IF 10.7) Pub Date : 2025-05-23 Honglin Xiong, Yulin Wang, Zhenrong Shen, Kaicong Sun, Yu Fang, Yan Chen, Dinggang Shen, Qian Wang
Magnetic Resonance Imaging (MRI) produces images with different contrasts, providing complementary information for clinical diagnoses and research. However, acquiring a complete set of MRI sequences can be challenging due to limitations such as lengthy scan time, motion artifacts, hardware constraints, and patient-related factors. To address this issue, we propose a novel method to learn Contrast and
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Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal consistency Med. Image Anal. (IF 10.7) Pub Date : 2025-05-18 Su Yan, Clotilde Vié, Marcelo Lerendegui, Herman Verinaz-Jadan, Jipeng Yan, Martina Tashkova, James Burn, Bingxue Wang, Gary Frost, Kevin G. Murphy, Meng-Xing Tang
Super-resolution ultrasound (SRUS) imaging through localisation and tracking of microbubble (MB), also known as ultrasound localisation microscopy (ULM), allows non-invasive imaging of microvasculature in vivo beyond the diffraction limit. The number of MBs localised from the acquired contrast-enhanced ultrasound (CEUS) images and the localisation accuracy precision directly influence the quality of
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Multi-view hybrid graph convolutional network for volume-to-mesh reconstruction in cardiovascular MRI Med. Image Anal. (IF 10.7) Pub Date : 2025-05-17 Nicolás Gaggion, Benjamin A. Matheson, Yan Xia, Rodrigo Bonazzola, Nishant Ravikumar, Zeike A. Taylor, Diego H. Milone, Alejandro F. Frangi, Enzo Ferrante
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational anatomy studies, biomarker discovery, and in-silico simulations. Traditional approaches typically follow complex multi-step pipelines, first segmenting images
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Automated motor-leg scoring in stroke via a stable graph causality debiasing model Med. Image Anal. (IF 10.7) Pub Date : 2025-05-15 Rui Guo, Xinyue Li, Miaomiao Xu, Lian Gu, Xiaohua Qian
Difficulty in resisting gravity is a common leg motor impairment in stroke patients, significantly impacting daily life. Automated clinical-level quantification of motor-leg videos based on the National Institutes of Health Stroke Scale is crucial for consistent and timely stroke diagnosis and assessment. However, real-world applications are challenged by interference impacting motion representation
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AttriMIL: Revisiting attention-based multiple instance learning for whole-slide pathological image classification from a perspective of instance attributes Med. Image Anal. (IF 10.7) Pub Date : 2025-05-14 Linghan Cai, Shenjin Huang, Ye Zhang, Jinpeng Lu, Yongbing Zhang
Multiple instance learning (MIL) is a powerful approach for whole-slide pathological image (WSI) analysis, particularly suited for processing gigapixel-resolution images with slide-level labels. Recent attention-based MIL architectures have significantly advanced weakly supervised WSI classification, facilitating both clinical diagnosis and localization of disease-positive regions. However, these methods
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A survey of deep-learning-based radiology report generation using multimodal inputs Med. Image Anal. (IF 10.7) Pub Date : 2025-05-13 Xinyi Wang, Grazziela Figueredo, Ruizhe Li, Wei Emma Zhang, Weitong Chen, Xin Chen
Automatic radiology report generation can alleviate the workload for physicians and minimize regional disparities in medical resources, therefore becoming an important topic in the medical image analysis field. It is a challenging task, as the computational model needs to mimic physicians to obtain information from multi-modal input data (i.e., medical images, clinical information, medical knowledge
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AdaptFRCNet: Semi-supervised adaptation of pre-trained model with frequency and region consistency for medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2025-05-13 Along He, Yanlin Wu, Zhihong Wang, Tao Li, Huazhu Fu
Recently, large pre-trained models (LPM) have achieved great success, which provides rich feature representation for downstream tasks. Pre-training and then fine-tuning is an effective way to utilize LPM. However, the application of LPM in the medical domain is hindered by the presence of a large number of parameters and a limited amount of labeled data. In clinical practice, there exists a substantial
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Driven by textual knowledge: A Text-View Enhanced Knowledge Transfer Network for lung infection region segmentation Med. Image Anal. (IF 10.7) Pub Date : 2025-05-12 Lexin Fang, Xuemei Li, Yunyang Xu, Fan Zhang, Caiming Zhang
Lung infections are the leading cause of death among infectious diseases, and accurate segmentation of the infected lung area is crucial for effective treatment. Currently, segmentation methods that rely solely on imaging data have limited accuracy. Incorporating text information enriched with expert knowledge into the segmentation process has emerged as a novel approach. However, previous methods
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Structure-guided MR-to-CT synthesis with spatial and semantic alignments for attenuation correction of whole-body PET/MR imaging Med. Image Anal. (IF 10.7) Pub Date : 2025-05-10 Jiaxu Zheng, Zhenrong Shen, Lichi Zhang, Qun Chen
Image synthesis from Magnetic Resonance (MR) to Computed Tomography (CT) can estimate the electron density of tissues, thereby facilitating Positron Emission Tomography (PET) attenuation correction in whole-body PET/MR imaging. Whole-body MR-to-CT synthesis faces several challenges including the spatial misalignment caused by tissue variety and respiratory movements, and the complex intensity mapping
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Next-generation surgical navigation: Marker-less multi-view 6DoF pose estimation of surgical instruments Med. Image Anal. (IF 10.7) Pub Date : 2025-05-10 Jonas Hein, Nicola Cavalcanti, Daniel Suter, Lukas Zingg, Fabio Carrillo, Lilian Calvet, Mazda Farshad, Nassir Navab, Marc Pollefeys, Philipp Fürnstahl
State-of-the-art research of traditional computer vision is increasingly leveraged in the surgical domain. A particular focus in computer-assisted surgery is to replace marker-based tracking systems for instrument localization with pure image-based 6DoF pose estimation using deep-learning methods. However, state-of-the-art single-view pose estimation methods do not yet meet the accuracy required for
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Error correcting 2D–3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images Med. Image Anal. (IF 10.7) Pub Date : 2025-05-10 Matthias Schwab, Mathias Pamminger, Christian Kremser, Daniel Obmann, Markus Haltmeier, Agnes Mayr
Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo reference standard for assessing infarct size (IS) and microvascular obstruction (MVO) in ST-elevation myocardial infarction (STEMI) patients. However, the exact quantification of those markers of myocardial infarct severity remains challenging and very time-consuming. As LGE distribution patterns can
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Nested hierarchical group-wise registration with a graph-based subgrouping strategy for efficient template construction Med. Image Anal. (IF 10.7) Pub Date : 2025-05-10 Tongtong Che, Lin Zhang, Debin Zeng, Yan Zhao, Haoying Bai, Jichang Zhang, Xiuying Wang, Shuyu Li
Accurate and efficient group-wise registration for medical images is fundamentally important to construct a common template image for population-level analysis. However, current group-wise registration faces the challenges posed by the algorithm’s efficiency and capacity, and adaptability to large variations in the subject populations. This paper addresses these challenges with a novel Nested Hierarchical
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Learning dissection trajectories from expert surgical videos via imitation learning with equivariant diffusion Med. Image Anal. (IF 10.7) Pub Date : 2025-05-10 Hongyu Wang, Yonghao Long, Yueyao Chen, Hon-Chi Yip, Markus Scheppach, Philip Wai-Yan Chiu, Yeung Yam, Helen Mei-Ling Meng, Qi Dou
Endoscopic Submucosal Dissection (ESD) constitutes a firmly well-established technique within endoscopic resection for the elimination of epithelial lesions. Dissection trajectory prediction in ESD videos has the potential to strengthen surgical skills training and simplify surgical skills training. However, this approach has been seldom explored in previous research. While imitation learning has proven
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SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images Med. Image Anal. (IF 10.7) Pub Date : 2025-05-08 Bella Specktor-Fadida, Liat Ben-Sira, Dafna Ben-Bashat, Leo Joskowicz
Quality control (QC) of structures segmentation in volumetric medical images is important for identifying segmentation errors in clinical practice and for facilitating model development by enhancing network performance in semi-supervised and active learning scenarios. This paper introduces SegQC, a novel framework for segmentation quality estimation and segmentation error detection. SegQC computes
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Confidence intervals for performance estimates in brain MRI segmentation Med. Image Anal. (IF 10.7) Pub Date : 2025-05-08 Rosana El Jurdi, Gaël Varoquaux, Olivier Colliot
Medical segmentation models are evaluated empirically. As such an evaluation is based on a limited set of example images, it is unavoidably noisy. Beyond a mean performance measure, reporting confidence intervals is thus crucial. However, this is rarely done in medical image segmentation. The width of the confidence interval depends on the test set size and on the spread of the performance measure
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CausalMixNet: A mixed-attention framework for causal intervention in robust medical image diagnosis Med. Image Anal. (IF 10.7) Pub Date : 2025-05-08 Yajie Zhang, Yu-An Huang, Yao Hu, Rui Liu, Jibin Wu, Zhi-An Huang, Kay Chen Tan
Confounding factors inherent in medical images can significantly impact the causal exploration capabilities of deep learning models, resulting in compromised accuracy and diminished generalization performance. In this paper, we present an innovative methodology named CausalMixNet that employs query-mixed intra-attention and key&value-mixed inter-attention to probe causal relationships between input
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PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation Med. Image Anal. (IF 10.7) Pub Date : 2025-05-07 Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra, Hendrik Mattern, Gábor Janiga, Oliver Speck, Andreas Nürnberger, Sahani Pathiraja
In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification
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REPAIR: Reciprocal assistance imputation-representation learning for glioma diagnosis with incomplete MRI sequences Med. Image Anal. (IF 10.7) Pub Date : 2025-05-06 Chuixing Wu, Jincheng Xie, Fangrong Liang, Weixiong Zhong, Ruimeng Yang, Yuankui Wu, Tao Liang, Linjing Wang, Xin Zhen
The absence of MRI sequences is a common occurrence in clinical practice, posing a significant challenge for prediction modeling of non-invasive diagnosis of glioma (GM) via fusion of multi-sequence MRI. To address this issue, we propose a novel unified reciprocal assistance imputation-representation learning framework (namely REPAIR) for GM diagnosis modeling with incomplete MRI sequences. REPAIR
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Rethinking boundary detection in deep learning-based medical image segmentation Med. Image Anal. (IF 10.7) Pub Date : 2025-05-06 Yi Lin, Dong Zhang, Xiao Fang, Yufan Chen, Kwang-Ting Cheng, Hao Chen
Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision. While current methods have shown promise in accurately segmenting major regions of interest, the precise segmentation of boundary areas remains challenging. In this study, we propose a novel network architecture named CTO, which combines Convolutional Neural Networks (CNNs), Vision Transformer
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Monocular pose estimation of articulated open surgery tools - in the wild Med. Image Anal. (IF 10.7) Pub Date : 2025-05-03 Robert Spektor, Tom Friedman, Itay Or, Gil Bolotin, Shlomi Laufer
This work presents a framework for monocular 6D pose estimation of surgical instruments in open surgery, addressing challenges such as object articulations, specularity, occlusions, and synthetic-to-real domain adaptation. The proposed approach consists of three main components: (1) synthetic data generation pipeline that incorporates 3D scanning of surgical tools with articulation rigging and physically-based
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XCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans Med. Image Anal. (IF 10.7) Pub Date : 2025-05-03 Lavsen Dahal, Mobina Ghojoghnejad, Liesbeth Vancoillie, Dhrubajyoti Ghosh, Yubraj Bhandari, David Kim, Fong Chi Ho, Fakrul Islam Tushar, Sheng Luo, Kyle J. Lafata, Ehsan Abadi, Ehsan Samei, Joseph Y. Lo, W. Paul Segars
Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VITs. However, the current libraries of computational phantoms face limitations, particularly in terms of sample size and heterogeneity. Insufficient representation of the population hampers
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Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation Med. Image Anal. (IF 10.7) Pub Date : 2025-05-02 Julio Silva-Rodríguez, Jose Dolz, Ismail Ben Ayed
The recent popularity of foundation models and the pre-train-and-adapt paradigm, where a large-scale model is transferred to downstream tasks, is gaining attention for volumetric medical image segmentation. However, current transfer learning strategies devoted to full fine-tuning for transfer learning may require significant resources and yield sub-optimal results when the labeled data of the target