Latest Medical Imaging Research Papers

The newest Medical Imaging papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Medical Imaging so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• 児童・青年期の反応性愛着障害における腹側線条体の機能不全：機能的MRI 研究
  Shinichiro Takiguchi, T Fujisawa, Sakae Mizushima, Daisuke Saito et al. · Institutional Repositories ... · Mar 1, 2027

  Background: Child maltreatment is a major risk factor for psychopathology, including reactive attachmentdisorder (RAD). Aims: To examine whether neural activity during reward processing was altered in children and adolescents with RAD. Meth…

• Unsupervised Deep-learning Methods for Low-dose Computed Tomography Reconstruction
  Ran An · Open MIND · Jan 1, 2027

  Computed tomography (CT) has become an indispensable imaging technique in medical diagnostics and industrial applications, owing to its non-invasive nature and high resolution in visualizing object internal structures. While X-ray CT (X-ray…

• Specificity of non-motor symptoms of Parkinson’s disease : A systematic review and meta-analysis
  Lancaster EPrints (Lancaste... · Dec 31, 2026

  Background: Middle-aged and older adults (>55 years old) have a high risk of developing Parkinson’s disease (PD); however, many non-motor symptoms (NMSs) indicative of PD are also common in middle-aged and older adults without a neurologica…

• Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation
  Yu Qi, Zhang Ye, Xinyi Xu, Yuxuan Lu et al. · arXiv · Jul 23, 2026

  Compositional generalization is essential for robot to follow diverse instructions. However, pretrained policies are known to take shortcuts, deferring to salient cues rather than grounding language. We introduce a diagnostic framework that…

• UnDA: Unpaired Domain Alignment for Cross-Modal Knowledge Transfer in Medical Imaging
  Rafsan Jany, Shadab Tanjeed Ahmad, Ahsan Bulbul, Tahsinul Islam et al. · arXiv · Jul 23, 2026

  Multimodal based approaches often outperform single modality approaches in downstream tasks as the different modalities provide complementary information, yet acquiring paired clinical data remains a significant challenge in real world scen…

• Future Rendering $\neq$ Future Surface: A Benchmark and Dataset for Dynamic Surface Reconstruction Beyond the Observed Window
  Yukun Shi, Minglun Gong · arXiv · Jul 23, 2026

  Dynamic-scene reconstruction is almost always evaluated inside the observed time window, yet deployment settings such as AR overlays, robot interaction, and anticipatory planning need the future surface: the geometry at times beyond those c…

• Self-supervision drives representational convergence in medical foundation models more than clinical supervision
  Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia, Lisa Adams et al. · arXiv · Jul 22, 2026

  Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, wha…

• SHFormer: Dynamic Spectral Filtering Convolutional Neural Network and High-pass Kernel Generation Transformer for Adaptive MRI Reconstruction
  Sriprabha Ramanarayanan, Rahul G. S., Mohammad Al Fahim, Keerthi Ram et al. · arXiv · Jul 22, 2026

  Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between distant pixel neighborhoods to compute feature representations. Accelerated MRI reconstruction benefits from AM, as t…

• PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image
  Dankai Liao, Tianyi Zhang, Yufeng Wu, Xinyue Zhang et al. · arXiv · Jul 21, 2026

  Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence. However, most existing pathology benchmarks evaluate models on pre-cropped p…

• CRISP: Constrained Refinement via Iterative Squeezing Process for Robust Medical Image Segmentation under Domain Shift
  Yizhou Fang, Pujin Cheng, Yixiang Liu, Xiaoying Tang et al. · arXiv · Jul 16, 2026

  Distribution shift in medical imaging remains a central bottleneck for the clinical translation of medical AI. Failure to address it can lead to severe performance degradation in unseen environments and exacerbate health inequities. Existin…

• DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology
  Héctor Carrión, Narges Norouzi · arXiv · Jul 14, 2026

  Dermatological practice routinely involves measuring and tracking lesion size, morphology and texture, as critical components of wound or skin cancer screening, monitoring and diagnosis. To accomplish this task, practitioners often image th…

• Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
  Héctor Carrión, Narges Norouzi · arXiv · Jul 14, 2026

  Accurate dermatological diagnosis naturally necessitates equitable performance across diverse populations, yet a systematic lack of expertly annotated images, especially for underrepresented skin tones and rare diseases, impedes progress to…

• Exact and Calibrated Diffusion Reconstruction for Digital Breast Tomosynthesis
  Imade Bouftini · arXiv · Jul 14, 2026

  Limited-angle digital breast tomosynthesis (DBT) reconstructs a volume from a few low-dose projections over a narrow arc. At a representative nine-view, $25^{\circ}$ protocol more than 98% of image space is unmeasured, so a learned prior mu…

• UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
  Yunzhou Li, Jiesi Hu, Yanwu Yang, Hanyang Peng et al. · arXiv · Jul 14, 2026

  Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fragmented by prompt paradigms and spatial dimensions. Visual in-context learning, interactive …

• ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
  Jiawen Li, Tian Guan, Huijuan Shi, Xitong Ling et al. · arXiv · Jul 10, 2026

  Foundation models are reshaping computational pathology, yet their capabilities remain shaped by pretraining objectives, data sources, and spatial scales, fragmenting complementary expertise across separate backbones. Here we present ALICE,…

• Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation
  Yungeng Liu, Xuanzi Fang, Haijin Zeng, Qi Dai et al. · arXiv · Jul 10, 2026

  Text-guided medical image segmentation leverages clinical semantics to improve lesion delineation, yet many existing models bind cross-modal fusion, supervision, and decoder design into a task-specific architecture. Such tight coupling make…

• Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction
  Ayda Eghbalian, Kevin Desai · arXiv · Jul 9, 2026

  Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable. However, most pose estimators remain optimized for geometric keypoint accuracy, while many real-world applicatio…

• MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models
  Hyunjae Kim, Dain Kim, Pan Xiao, Serina S. Applebaum et al. · arXiv · Jul 8, 2026

  Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundation models is constrained by limited access to large-scale, high-quality clinical data. …

• Cardiac MRI Through-Plane Super-Resolution Guided by Reference and Memory
  Shaoming Pan, Chenchuhui Hu, Leon Axel, Meng Ye · arXiv · Jul 8, 2026

  Clinical cardiac MRI is commonly acquired with high in-plane resolution but coarse through-plane resolution to reduce scan time and accommodate breath-hold and cardiac-motion constraints, which limits 3D analysis and diagnostic accuracy. We…

• Automatic Echocardiography Segmentation via Transition Probability Correlation for Stable Semantic Extraction
  Xinran Chen, Xiyuan Wang, Guangquan Zhou, Chuan Chen · arXiv · Jul 8, 2026

  While echocardiography is essential for cardiovascular diagnosis, inherent speckle noise and low signal-to-noise ratio often lead to ambiguous semantic features and fragmented boundaries. These limitations significantly hinder the segmentat…

• AA-ViT: Anatomically Aware Vision Transformer with Structural and Frequency Guidance for Contrast Enhanced Brain MRI Synthesis
  Talha Meraj, Tom Flannery, Charlie Cummins, Matt Townend et al. · arXiv · Jul 8, 2026

  Accurate tumour localization and diagnosis is a critical component of clinical care for brain cancers. Magnetic Resonance Imaging (MRI) is the most commonly used imaging modality due to its superior soft-tissue contrast. However, standard M…

• VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentation
  Zimu Zhang, Yiheng Zhong, Zhuoru Zhang, Yingzhen Hu et al. · arXiv · Jul 8, 2026

  Semi-supervised 3D medical image segmentation reduces the need for dense voxel-level annotations by exploiting unlabeled volumes. Although existing methods such as consistency regularization, pseudo-labeling, and co-training improve predict…

• Heterogeneity-Adaptive Diffusion Schrodinger Bridge for PET-Guided Whole-Body MRI Translation
  Chengbo Wang, Jiacheng Yu, Linjie Bian, Ming Qi et al. · arXiv · Jul 8, 2026

  While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acquisition time in PET-MR scanning is a major obstacle in more efficient clinical practice. …

• Unsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasets
  Xuan Liu, Derek L. Nguyen, Emily C. Barre, Jennifer Thomas et al. · arXiv · Jul 7, 2026

  Deep learning-based computer-aided diagnosis (CAD) systems have shown strong performance in breast cancer diagnosis, particularly for classification tasks in mammography. However, domain shifts across multi-site datasets remain a challenge,…

• MARVEL: Margin-Aware Robust von Mises-Fischer Expert Learning for Long-Tailed Out-of-Distribution Detection
  A. S. Anudeep, Vaanathi Sundaresan · arXiv · Jul 2, 2026

  For clinical deployment, it is essential that automated diagnostic systems remain reliable when confronted with previously unseen cases, yet deep models routinely misclassify out-of-distribution (OOD) inputs with high confidence, underscori…

• Self-Auditing Residual Drifting for Pathology-Preserving Accelerated Knee MRI
  Qing Lyu, Jianxu Wang, Mohammad Kawas, Ge Wang et al. · arXiv · Jul 2, 2026

  Accelerated magnetic resonance imaging reduces acquisition time, but reconstruction from undersampled k-space can blur diagnostically relevant structures or introduce failures that are not captured by global image metrics. We propose SA-RDM…

• High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
  Yu Guan, Tianjia Huang, Qinrong Cai, Qiuyun Fan et al. · arXiv · Jul 1, 2026

  Magnetic resonance imaging (MRI) reconstruction under realistic acquisition conditions can be fundamentally viewed as estimating the underlying k-space distribution from incomplete and noise-corrupted measurements. While diffusion models ha…

• APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
  Juntao Jiang, Jinsheng Bai, Linxuan Fan, Yali Bi et al. · arXiv · Jun 29, 2026

  We present APRIL-MedSeg, a YAML-driven modular framework for 2D medical image segmentation. It provides a unified and extensible ecosystem that decomposes segmentation networks into reusable components. Also, the framework integrates a broa…

• EchoSonar-R: A Multi-View Reasoning-Enabled Model for Disease Classification and Report Generation in Echocardiography
  Darya Taratynova, Ahmed Aly, Numan Saeed, Mohammad Yaqub · arXiv · Jun 26, 2026

  Echocardiography is the most widely used non-invasive cardiac imaging modality, providing essential information for cardiovascular diagnosis. Interpreting an echocardiogram requires synthesizing complementary evidence across multiple heart …

• CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs
  Hashmat Shadab Malik, Anees Ur Rehman Hashmi, Numan Saeed, Muzammal Naseer et al. · arXiv · Jun 25, 2026

  Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text judged only by its final answer, making it hard to interpret and verify, especially in 3D r…