#Medical Imaging
Latest news articles tagged with "Medical Imaging". Follow the timeline of events, related topics, and entities.
Articles (12)
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🇺🇸 Unsupervised Denoising of Diffusion-Weighted Images with Bias and Variance Corrected Noise Modeling
[USA]
arXiv:2602.22235v1 Announce Type: cross Abstract: Diffusion magnetic resonance imaging (dMRI) plays a vital role in both clinical diagnostics and neuroscience research. However, its inherently low si...
Related: #Artificial Intelligence, #Image Processing -
🇺🇸 Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention
[USA]
arXiv:2602.22381v1 Announce Type: cross Abstract: Accurate prediction of malignancy in renal tumors is crucial for informing clinical decisions and optimizing treatment strategies. However, existing ...
Related: #Medical AI, #Deep Learning, #Cancer Diagnosis -
🇺🇸 HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography
[USA]
arXiv:2602.22544v1 Announce Type: cross Abstract: Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging, but low-dose acquisition introduces strong, spatially varyin...
Related: #Artificial Intelligence, #Image Processing -
🇺🇸 OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation
[USA]
arXiv:2602.20752v1 Announce Type: cross Abstract: Musculoskeletal disorders represent a significant global health burden and are a leading cause of disability worldwide. While MRI is essential for ac...
Related: #AI in Medicine, #Healthcare Technology -
🇺🇸 MIP Candy: A Modular PyTorch Framework for Medical Image Processing
[USA]
arXiv:2602.21033v1 Announce Type: cross Abstract: Medical image processing demands specialized software that handles high-dimensional volumetric data, heterogeneous file formats, and domain-specific ...
Related: #Software Development, #Artificial Intelligence -
🇺🇸 Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures
[USA]
arXiv:2602.20994v1 Announce Type: cross Abstract: Report-supervised (RSuper) learning seeks to alleviate the need for dense tumor voxel labels with constraints derived from radiology reports (e.g., v...
Related: #Artificial Intelligence, #Healthcare Technology -
🇺🇸 Foundation Models for Medical Imaging: Status, Challenges, and Directions
[USA]
arXiv:2602.15913v1 Announce Type: cross Abstract: Foundation models (FMs) are rapidly reshaping medical imaging, shifting the field from narrowly trained, task-specific networks toward large, general...
Related: #Foundation Models, #Cross‑modal Adaptation, #Design Principles, #Future Directions -
🇺🇸 Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis
[USA]
arXiv:2602.15067v1 Announce Type: new Abstract: Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology, making treatment challenging due to compl...
Related: #Deep Learning, #Neuro-Oncology, #Computer Vision, #Survival Prediction -
🇺🇸 Task-Agnostic Continual Learning for Chest Radiograph Classification
[USA]
arXiv:2602.15811v1 Announce Type: cross Abstract: Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on previo...
Related: #Artificial Intelligence in Healthcare, #Continual Learning, #Chest X‑ray Diagnosis, #Model Deployment and Updating -
🇺🇸 Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement
[USA]
arXiv:2602.12317v1 Announce Type: cross Abstract: Medical image foundation models (MIFMs) have demonstrated remarkable potential for a wide range of clinical tasks, yet their development is constrain...
Related: #Medical AI, #Data Synthesis -
🇺🇸 Deep-Learning Atlas Registration for Melanoma Brain Metastases: Preserving Pathology While Enabling Cohort-Level Analyses
[USA]
arXiv:2602.12933v1 Announce Type: cross Abstract: Melanoma brain metastases (MBM) are common and spatially heterogeneous lesions, complicating cohort-level analyses due to anatomical variability and ...
Related: #Medical Technology, #Artificial Intelligence, #Cancer Research -
🇺🇸 Improved cystic hygroma detection from prenatal imaging using ultrasound-specific self-supervised representation learning
[USA]
arXiv:2512.22730v2 Announce Type: cross Abstract: Cystic hygroma is a high-risk prenatal ultrasound finding that portends high rates of chromosomal abnormalities, structural malformations, and advers...
Related: #Artificial Intelligence, #Prenatal Healthcare