Medical Imaging and Analysis

Neueste Paper

• MirrorNet: Can Medical Image Anonymization Really Protect Patient Identity?

  Attila Simk\'o · 7. August 2026

  Medical images are routinely de-identified---names, dates, and other metadata removed---and then shared for research, teaching, and public benchmarks under the assumption that this renders them anonymous. Such de-identification protects the metadata but not the pixels, and---apart from scans that di…

• OsteoCAD: A Human-in-the-Loop Cloud-Edge Framework for Bone Tumor Segmentation

  Maximo Rodriguez-Herrero, Dante D. Sanchez-Gallegos, Heriberto Aguirre-Meneses, Marco Antonio N\'u\~nez-Gaona, J. L. Gonzalez-Compean, Jesus Carretero · 3. August 2026

  Artificial Intelligence (AI) and Deep Learning (DL) have notably advanced medical image analysis, yet many health- care organizations struggle to adopt them due to limited com- putational resources and specialized expertise. To address these barriers, we introduce OsteoCAD, a modular eHealth framewo…

• Anatomy Contextualized Adaption of CT Foundation Models

  Roshan Kenia, Stephanie L McNamara, William Lotter · 30. Juli 2026

  CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-grained anatomical signals. Fine-grained vision-language pre-training addresses this by aligning anatomy-level visual fea…

• DS@GT ARC at ImageCLEFmed GANs 2026: Geometric Filtering for Privacy-Preserving CT Slice Generation

  Eric Regina, Richard Arnaud, Samir Hadi Cisneros · 24. Juli 2026

  We present a privacy-preserving framework for synthetic lung CT slice generation developed for the Image-CLEFmed GANs 2026 challenge. The approach combines Optimal Transport Conditional Flow Matching with privacy-oriented training and a post-generation "Supervisor" pipeline that filters generated ca…

• Projection-Domain Sensitivity Analysis of Vertebral DRRs Under Intrinsic Calibration Perturbation

  Lin Li, Chaochao Zhou, Benjamin Aubert, Junlin Guo, Junchao Zhu · 14. Juli 2026

  Accurate geometric calibration is essential for fluoroscopy-guided spinal imaging, digitally reconstructed radiograph (DRR) generation, and 2D--3D vertebral registration. Although calibration quality is typically evaluated using reconstruction-based metrics such as reprojection error, its influence …

• False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation

  Linus Juni, Aasa Feragen, Aditya Parikh · 10. Juli 2026

  Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that auditing these segmentation tasks is complicated by a common property of modern segmentation datasets: expert-annotated gold labels are expensive, so a…

• A multi-agent system for spine MRI report generation from multi-sequence imaging

  Zhiping Xiao, Junwei Yang, Gongbo Sun, Han Zhang, Hanwen Xu, Yi Yao, Zachary D. Miller, William E. King III, Mohammed M. Kanani, Jalal B. Andre, Sammy Chu, Ming Zhang, Paul E. Kinahan, Nathan M. Cross, Sheng Wang · 9. Juni 2026

  Spinal pathology is a leading cause of pain and disability worldwide. Spine MRI is central to clinical evaluation, yet its interpretation remains complex and time-consuming, requiring integration of information across multiple imaging sequences and anatomical regions. Despite recent advances in auto…

• Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction

  Himanshu Singh · 8. Juni 2026

  Numerous medical imaging problems must be solved under limited labels and constrained compute, yet it remains unclear whether performance gains are driven mainly by more expressive models or by better representation of clinically meaningful anatomy. We study this question through a low-data anatomy-…

• Cross-Source Supervision for Bone Infection Segmentation in Dual-Modality PET-CT

  Zonglin Yang, Xiaolei Diao, Jishizhan Chen, Xiaozhuang Man, Wei Kong, Gen Wen, Pengfei Cheng, Daqian Shi · 19. Mai 2026

  Early and accurate diagnosis and lesion localization of bone infections are crucial for clinical treatment. PET-CT integrates anatomical information from CT with metabolic information from PET, making it an important imaging modality for diagnosing bone infections. However, accurate lesion segmentat…

• Unsupervised Machine Learning for Osteoporosis Diagnosis Using Singh Index Clustering on Hip Radiographs

  Vijaya Kalavakonda, Vimaladevi Madhivanan, Abhay Lal, Senthil Rithika, Shamala Karupusamy Subramaniam, Mohamed Sameer · 1. Mai 2026

  Osteoporosis, a prevalent condition among the aging population worldwide, is characterized by diminished bone mass and altered bone structure, increasing susceptibility to fractures. It poses a significant and growing global public health challenge over the next decade. Diagnosis typically involves …

• 3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography

  Weicheng Zhu, Haoxu Huang, Huanze Tang, Rushabh Musthyala, Boyang Yu, Long Chen, Emilio Vega, Thomas O'Donnell, Seena Dehkharghani, Jennifer A. Frontera, Arjun V. Masurkar, Kara Melmed, Narges Razavian · 22. April 2026

  Head computed tomography (CT) imaging is a widely-used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull, and cerebrovascular system. It is commonly the first-line imaging in neurologic emergencies given its rapidity of image acquisition…

• Intelligent Healthcare Imaging Platform: A VLM-Based Framework for Automated Medical Image Analysis and Clinical Report Generation

  Samer Al-Hamadani · 20. April 2026

  The rapid advancement of artificial intelligence (AI) in healthcare imaging has revolutionized diagnostic medicine and clinical decision-making processes. This work presents an intelligent multimodal framework for medical image analysis that leverages Vision-Language Models (VLMs) in healthcare diag…

• MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI

  Paula Arguello, Berk Tinaz, Mohammad Shahab Sepehri, Maryam Soltanolkotabi, Mahdi Soltanolkotabi · 14. April 2026

  Deep learning underpins a wide range of applications in MRI, including reconstruction, artifact removal, and segmentation. However, progress has been driven largely by public datasets focused on brain and knee imaging, shaping how models are trained and evaluated. As a result, careful studies of the…

• An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis

  Md. Sajeebul Islam Sk., Md. Mehedi Hasan Shawon, Md. Golam Rabiul Alam · 6. April 2026

  Lumbar Spinal Stenosis (LSS) diagnosis remains a critical clinical challenge, with diagnosis heavily dependent on labor-intensive manual interpretation of multi-view Magnetic Resonance Imaging (MRI), leading to substantial inter-observer variability and diagnostic delays. Existing vision-language mo…

• Refining 3D Medical Segmentation with Verbal Instruction

  Kangxian Xie, Jiancheng Yang, Nandor Pinter, Chao Wu, Behzad Bozorgtabar, Mingchen Gao · 17. März 2026

  Accurate 3D anatomical segmentation is essential for clinical diagnosis and surgical planning. However, automated models frequently generate suboptimal shape predictions due to factors such as limited and imbalanced training data, inadequate labeling quality, and distribution shifts between training…

• Prompting with the human-touch: evaluating model-sensitivity of foundation models for musculoskeletal CT segmentation

  Caroline Magg, Maaike A. ter Wee, Johannes G. G. Dobbe, Geert J. Streekstra, Leendert Blankevoort, Clara I. S\'anchez, Hoel Kervadec · 12. März 2026

  Promptable Foundation Models (FMs), initially introduced for natural image segmentation, have also revolutionized medical image segmentation. The increasing number of models, along with evaluations varying in datasets, metrics, and compared models, makes direct performance comparison between models …

• Survey of Computerized Adaptive Testing: A Machine Learning Perspective

  Yan Zhuang, Qi Liu, Haoyang Bi, Zhenya Huang, Weizhe Huang, Jiatong Li, Junhao Yu, Zirui Liu, Zirui Hu, Yuting Hong, Zachary A. Pardos, Haiping Ma, Mengxiao Zhu, Shijin Wang, Enhong Chen · 10. März 2026

  Computerized Adaptive Testing (CAT) offers an efficient and personalized method for assessing examinee proficiency by dynamically adjusting test questions based on individual performance. Compared to traditional, non-personalized testing methods, CAT requires fewer questions and provides more accura…

• Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning

  Federico Gabriele, Aldo Glielmo, Marco Taboga · 17. Februar 2026

  Current macroeconomic models with agent heterogeneity can be broadly divided into two main groups. Heterogeneous-agent general equilibrium (GE) models, such as those based on Heterogeneous Agent New Keynesian (HANK) or Krusell-Smith (KS) approaches, rely on GE and 'rational expectations', somewhat u…

• Ultrasound-Guided Real-Time Spinal Motion Visualization for Spinal Instability Assessment

  Feng Li, Yuan Bi, Tianyu Song, Zhongliang Jiang, Nassir Navab · 16. Februar 2026

  Purpose: Spinal instability is a widespread condition that causes pain, fatigue, and restricted mobility, profoundly affecting patients' quality of life. In clinical practice, the gold standard for diagnosis is dynamic X-ray imaging. However, X-ray provides only 2D motion information, while 3D modal…

• Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement

  Yuhan Wei, Yuting He, Linshan Wu, Fuxiang Huang, Junlin Hou, Hao Chen · 16. Februar 2026

  Medical image foundation models (MIFMs) have demonstrated remarkable potential for a wide range of clinical tasks, yet their development is constrained by the scarcity, heterogeneity, and high cost of large-scale annotated datasets. Here, we propose RaSD (Randomized Synthesis and Disentanglement), a…

• Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification

  Fabi Nahian Madhurja, Rusab Sarmun, Muhammad E. H. Chowdhury, Adam Mushtak, Israa Al-Hashimi, Sohaib Bassam Zoghoul · 22. Januar 2026

  Cervical spine fractures are critical medical conditions requiring precise and efficient detection for effective clinical management. This study explores the viability of 2D projection-based vertebra segmentation for vertebra-level fracture detection in 3D CT volumes, presenting an end-to-end pipeli…

• Geometry-induced Regularization in Deep ReLU Neural Networks

  Joachim Bona-Pellissier (MaLGA), Fran\c{c}ois Malgouyres (IMT), Fran\c{c}ois Bachoc (LPP) · 6. Januar 2026

  Neural networks with a large number of parameters often do not overfit, owing to implicit regularization that favors \lq good\rq{} networks. Other related and puzzling phenomena include properties of flat minima, saddle-to-saddle dynamics, and neuron alignment. To investigate these phenomena, we stu…

• A-QCF-Net: An Adaptive Quaternion Cross-Fusion Network for Multimodal Liver Tumor Segmentation from Unpaired Datasets

  Arunkumar V, Firos V M, Senthilkumar S, Gangadharan G R · 29. Dezember 2025

  Multimodal medical imaging provides complementary information that is crucial for accurate delineation of pathology, but the development of deep learning models is limited by the scarcity of large datasets in which different modalities are paired and spatially aligned. This paper addresses this fund…

• Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models

  Kuofeng Gao, Yufei Zhu, Yiming Li, Jiawang Bai, Yong Yang, Zhifeng Li, Shu-Tao Xia · 24. Dezember 2025

  Text-to-image (T2I) diffusion models enable high-quality image generation conditioned on textual prompts. However, fine-tuning these pre-trained models for personalization raises concerns about unauthorized dataset usage. To address this issue, dataset ownership verification (DOV) has recently been …

• End2Reg: Learning Task-Specific Segmentation for Markerless Registration in Spine Surgery

  Lorenzo Pettinari, Sidaty El Hadramy, Michael Wehrli, Philippe C. Cattin, Daniel Studer, Carol C. Hasler, Maria Licci · 16. Dezember 2025

  Purpose: Intraoperative navigation in spine surgery demands millimeter-level accuracy. Current systems based on intraoperative radiographic imaging and bone-anchored markers are invasive, radiation-intensive and workflow disruptive. Recent markerless RGB-D registration methods offer a promising alte…