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

• Preclinical evaluation of PSMA-targeted ultrasound contrast agents in an orthotopic model of prostate cancer in rabbits.
  Felipe M. Berg, Eric Abenojar, Pinunta Nittayacharn, Nathan K. Hoggard et al. · PubMed · Jan 1, 2027

  The localization of prostate cancer by ultrasound remains limited by the lack of B-mode conspicuity and the confinement of clinically approved microbubbles (MBs) to the vasculature. This precludes differentiating viable tumor, necrotic tiss…

• Unsupervised Deep-learning Methods for Low-dose Computed Tomography Reconstruction
  Ran An · University of Liverpool · 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…

• Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.
  Muhammad Ahsan Hayat, Jahangir Baig, Shayan Ahmed, Ahmed Faraz Ayubi · Zenodo (CERN European Organ... · Nov 3, 2026

  Early detection of disease is a cornerstone for improving patient outcomes, reducing costs, and enabling preventative interventions. Traditional predictive models often rely on a single type of data (e.g., imaging, clinical labs, or genomic…

• Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.
  Muhammad Ahsan Hayat, Jahangir Baig, Shayan Ahmed, Ahmed Faraz Ayubi · Zenodo (CERN European Organ... · Nov 3, 2026

  Early detection of disease is a cornerstone for improving patient outcomes, reducing costs, and enabling preventative interventions. Traditional predictive models often rely on a single type of data (e.g., imaging, clinical labs, or genomic…

• Mapping of individual somatosensory representations - comparison of fMRI and TMS.
  Juha Gogulski, Mikko Nyrhinen, Rasmus Zetter, Maksym Tokariev et al. · PubMed · Nov 1, 2026
• Dosimetric and Clinical Impact of Bone Marrow-sparing Radiation Therapy for Anal Cancer: A Systematic Review.
  Jasmine Chen, Elizabeth Forde, Michelle Leech, Shao Hui Huang et al. · PubMed · Oct 1, 2026
• Cross-section measurements of lanthanum radioisotopes towards an optimized production of 133La and 135La for theranostic applications.
  Gaia Dellepiane, Lars Eggimann, Alexander Gottstein, Samuel Juillerat et al. · Open Access CRIS of the Uni... · Oct 1, 2026

  The theranostic pair 133La/135La has recently attracted growing interest as a promising candidate for combined diagnostic and therapeutic applications, owing to the favorable nuclear and chemical properties of lanthanum. 135La (t1/2 = 18.91…

• Characterizing cutaneous manifestations of systemic autoimmune rheumatologic diseases in Filipino skin.
  Geraldine T Zamora, Josef Symon S Concha, Juan Raphael M Gonzales, Arunee H Siripunvarapon et al. · PubMed · Oct 1, 2026

  Background: Cutaneous findings provide important clues to systemic autoimmune rheumatologic diseases (SARDs), but presentation varies across skin types and may be underrecognized in patients of color. In the Philippines, diagnostic challeng…

• A critical perspective on finite sample conformal prediction theory in medical applications.
  Klaus-Rudolf Kladny, Bernhard Schölkopf, Lisa Koch, Christian F Baumgartner et al. · Open Access CRIS of the Uni... · Oct 1, 2026

  Machine learning (ML) is transforming healthcare, but safe clinical decisions demand reliable uncertainty estimates that standard ML models fail to provide. Conformal prediction (CP) is a popular tool that allows users to turn heuristic unc…

• Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits
  John Bonnici, Matthew Baugh, Aleksandra Kulbaka, Sarah Cechnicka et al. · arXiv · Sep 10, 2026

  Accurate identification of end-diastole (ED) and end-systole (ES) in echocardiography underpins the quantification of ventricular function, yet manual selection of these key frames is subjective and introduces clinically significant inter-o…

• A Comparative Evaluation of Pre-trained Convolutional Neural Networks for Melanoma Detection
  Wagner Moreno Schmitz, Marco Antonio de Castro Barbosa, Thiago Magalhães Amaral, Dalcimar Casanova et al. · arXiv · Sep 10, 2026

  Early diagnosis of melanoma is critical for improving patient survival rates. However, accurately distinguishing melanoma from other skin lesions remains a significant clinical challenge due to the high visual similarity among lesion types …

• Breaking the Central Bias: Spatially Partitioned Experts for Coordinate-Based Neuroevolution
  Romain Claret, Arthur Gygax, Michael O'Neill, Paul Cotofrei et al. · arXiv · Sep 10, 2026

  Evolvable-Substrate HyperNEAT (ES-HyperNEAT), a bio-inspired indirect encoding that determines neuron placement and connection weights from spatial coordinates, exhibits a failure mode on MNIST as a diagnostic benchmark. Because input pixel…

• BruNet: A Cross-Domain Transfer Framework for Bruise Segmentation
  Qiming Wang, Richard J. Motley, Ebube E. Obi, Xianfang Sun et al. · arXiv · Sep 10, 2026

  Segmenting bruises is a challenging task in medical imaging due to limited data and annotations, diffuse boundaries, and highly variable appearance. In this work, we propose BruNet, a segmentation framework that combines a ViT-based visual …

• DINO-Med: A Unified Patch-Based Adaptation Framework for Multi-Modal Medical Image Analysis Applied to Liver Fibrosis Staging
  Boya Wang, Ruizhe Li, Chao Chen, Xin Chen · arXiv · Sep 10, 2026

  Adapting natural-image foundation models like DINOv3 to multi-modal medical imaging is challenging due to the significant domain gap between natural color images and multi-channel medical scans. We present a unified, patch-based framework t…

• Brain-PACE: A Deep Siamese MRI Framework for Modelling Longitudinal Brain Acceleration
  Samuel Maddox, Jacob Newman, Saber Sami, Michal Mackiewicz et al. · arXiv · Sep 10, 2026

  Brain age estimation has become a popular research proxy for assessing brain health and disease, yet longitudinal trajectories of brain ageing are still poorly defined, and clinical use is limited. Building on existing Siamese longitudinal …

• Mi-Ripple: Restoring Images Degraded by Iterative AI Editing
  Jiayin Chen, Yicheng Xu, Muting Wang · arXiv · Sep 10, 2026

  Iterative reference-conditioned image editing can introduce grid-like and granular textures, commonly described as digital ripple. We present Mi-Ripple, a diagnosis-guided restoration workflow that suppresses this digital ripple while prote…

• GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT
  Haojie Yang, Ran Su · arXiv · Sep 10, 2026

  Lung cancer causes more deaths than any other malignancy, and low-dose CT screening is the main pathway to early diagnosis. That pathway hinges on the smallest lesions, yet nodules below six millimeters remain hard to detect, because most m…

• Your Model Already Knows Don't Teach It, Learn to Ask It: Soft Prompting for Few-Shot Adaptation of Vision-Language Models
  Gautam Rajendrakumar Gare, Siyi Li, Hewei Wang, Cesar Daniel Hernandez et al. · arXiv · Sep 10, 2026

  We address few-shot object detection with vision-language models (VLMs) in out-of-domain settings such as aerial, industrial, and medical imagery, using only ten annotated images for supervision. Existing adaptation methods are discrete pro…

• Order-Aware 2.5D Multiple Instance Learning for Preoperative MRI-Based Perineural Invasion Risk Assessment in Intrahepatic Cholangiocarcinoma
  Hyunsu Go, Youngung Han, Kyeonghun Kim, Jinyong Jun et al. · arXiv · Sep 10, 2026

  Perineural invasion (PNI) is an adverse histopathologic marker in intrahepatic cholangiocarcinoma (ICC), but it is usually confirmed only after resection. Preoperative T2-weighted MRI may provide noninvasive imaging cues predictive of PNI, …

• SCINTILLA-SNN: A Spiking Multi-Scale Selective Aggregation Network for Perineural Invasion Prediction
  Youngung Han, Yului Jeong, Kyeonghun Kim, Dohyun Kweon et al. · arXiv · Sep 10, 2026

  Preoperative prediction of perineural invasion (PNI) in cholangiocarcinoma (CCA) is clinically valuable but remains challenging because PNI-related cues on magnetic resonance imaging (MRI) are subtle, sparse, and spatially localized around …

• CEM-TUDASR: Computationally efficient multi-modality transformer based unsupervised domain adaptive super-resolution approach
  Anjali Sarvaiya, Jay Kadel, Kishor Upla, Kiran Raja · arXiv · Sep 10, 2026

  Wireless Capsule Endoscopy (WCE) enables non-invasive visualization of the gastrointestinal tract, but its miniaturized optics, sensor limitations, and wireless transmission constraints result in low-resolution images with reduced visibilit…

• BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models
  Junfeng Xia, Wenhao Ye, Junxiang Zhang, Jiayu Zuo et al. · arXiv · Sep 9, 2026

  fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream tasks are adapted independently. We study…

• AgroVisNet: A lightweight Convolutional Network and the BD-PlantDX Expert-Validated Benchmark for Radish, Potato and Pointed Gourd Disease Classification
  Md. Abdullah Mandal, Saad Ahmed, Md. Khalid Syfullah · arXiv · Sep 9, 2026

  Automated plant disease diagnosis is increasingly deployed on farmer-held devices in regions where agronomic expertise is scarce and network connectivity is unreliable. Three obstacles limit its practical value: public benchmarks are domina…

• Advanced Brain Tissue Imaging with Data-Consistent Diffusion Priors in Laminographic X-Ray Nanoimaging
  Wenxuan Fang, Abraham L. Levitan, Ana Diaz, Carles Bosch et al. · arXiv · Sep 9, 2026

  Nanoscale imaging of mammalian brains is critical for connectomics. X-ray laminography enables high-throughput imaging of extended, plate-like biological specimens. However, the tilted acquisition geometry leads to incomplete Fourier-space …

• Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction
  Pranav Poudel, Florence Dell'Aniello Picard, Nairouz Shehata, Frédéric Lavoie et al. · arXiv · Sep 9, 2026

  Sparse-view X-ray 3D reconstruction is essential for reducing radiation exposure, but recovering a density field from a handful of X-ray projections is severely ill-posed. Recently, 3D Gaussian Splatting has achieved state-of-the-art perfor…

• A Joint 2D-3D Statistical Shape Model for Orthopedic Reconstruction
  Florence Dell'Aniello Picard, Pranav Poudel, Nairouz Shehata, Frédéric Lavoie et al. · arXiv · Sep 8, 2026

  Three-dimensional femoral reconstruction from radiographs supports surgical planning, implant sizing, and post-operative follow-up, but remains ill-posed as X-ray projections discard depth information. Existing methods often incorporate a 3…

• A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks
  Reza Rajabli, D. Louis Collins · arXiv · Sep 4, 2026

  When there is not enough labeled data to properly train deep learning models, transfer learning can help. We still do not fully understand how effective it is in neuroimaging, especially for Alzheimer's disease research. It is also not clea…

• What Matters, When? Diagnosing and Improving Conditional Visual Grounding in Visuomotor Imitation Policies
  Vivek Chavan, Pengtao Xie, Yahuan Shi, Oliver Heimann et al. · arXiv · Sep 4, 2026

  Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail when visually similar objects or receptacles are introduced. We study this behavior as a problem of conditional visual grounding: th…

• Cross-Domain Tracker Adaptation Without Target-Domain Labels via Vision-Language Agents
  Daniel Davila, Ravikumar Balakrishnan, Mike Cochran · arXiv · Sep 4, 2026

  We present a system that uses a Vision-Language Model (VLM) as a diagnostic agent for adapting a detect-to-track pipeline to a new target domain without access to target-domain labels. Rather than optimizing against annotated metrics, the V…

• TAP-Path: Task-Adaptive Structural and Token Pruning for Efficient and Trustworthy Pathology Foundation Models
  Mehedi Hasan, Ashfak Yeafi, Md Khairul Islam · arXiv · Sep 3, 2026

  Pathology foundation models improve transferable representation learning for histopathology, but recent gains often rely on encoders with hundreds of millions of parameters and high inference cost. We propose TAP-Path, a task-adaptive compr…