Topic
medical imaging
ProMUSE: Progressive Multi-modal Uncertainty-guided Staged Evidential Alzheimer Disease Classification
ProMUSE is a progressive multi-modal uncertainty-guided staged evidential network for Alzheimer disease classification. It uses low-cost clinical data first, then decides when to incorporate expensive MRI or PET imaging based on uncertainty, reducing imaging usage by 50-90% while maintaining accuracy.
Controlled Benchmark Finds No Quantum Advantage in Brain MRI Data Augmentation
A controlled benchmark study by Haider and Figini shows that quantum-latent GAN augmentation does not improve brain MRI classification over real-data-only training or classical GANs. The quantum and classical generators were statistically indistinguishable across all data fractions from 5% to 100%.
Breast MRI AI Challenge Reveals Trade-Offs Between Accuracy and Fairness Across Patient Subgroups
The MAMA-MIA Challenge provided a standardized benchmark for breast MRI tumor segmentation and pathologic complete response prediction. Using a training cohort of 1,506 patients from US institutions and an external test set of 574 patients from three European centers, 26 international teams showed substantial performance variability and trade-offs between overall accuracy and subgroup fairness across age, menopausal status, and breast density.
Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning
A study proposes an interpretable deep learning framework combining EfficientNet-B0 with a Convolutional Block Attention Module for sperm morphology classification, achieving 90.2% and 93.9% accuracy on SMIDS and HuSHem datasets respectively.
BrainG3N Tokenizer Enables Controllable 3D Brain MRI Generation with Clinical-Grade Embeddings
BrainG3N, a novel tokenizer for 3D brain MRI latent diffusion, decouples encoder and decoder to preserve clinical information while enabling high-quality reconstruction. Pretrained on 35,309 volumes, it outperforms SOTA models on 21 of 23 clinical tasks and supports controllable generation for disease simulation and privacy-preserving data sharing.
First Billion-Parameter Generative Foundation Model for Chest Radiography Achieves Expert-Level Synthesis Fidelity
Ribeiro et al. present the largest specialist generative foundation model for chest radiographs, with over 1.3 billion parameters. Trained on 1.2 million radiographs, the model supports controllable generation across demographics, views, and pathologies, advancing synthesis fidelity to clinical indistinguishability.
New AI Framework PSCT-Net Reduces Radiation Risk in Pediatric Skull CT Imaging
PSCT-Net is a novel deep learning framework for reconstructing 3D CT scans of pediatric skulls from only two X-ray images, significantly reducing radiation exposure. The method uses differentiable back-projection and attention-guided refinement to overcome depth ambiguity. It was evaluated on a private dataset called PedSkull-CT.
New AI Framework Synthesizes Fluorescein Angiography from Fundus and Sparse OCT Scans
A research team led by Ma introduced a novel deep learning framework that synthesizes fluorescein angiography (FFA) from color fundus photography (CFP) using structural guidance from sparse optical coherence tomography (OCT) scans. The method uses a tri-modally aligned dataset of 3,676 patient eyes and achieves superior synthesis and downstream diagnosis performance compared to existing methods.
Input-Dependent Fisher Information Enables Local Sensitivity Analysis of Medical Image Classifiers
A research paper introduces a local sensitivity analysis framework based on the input-dependent Fisher Information Matrix (iFIM) for medical image classifiers. The method projects input images into high- and low-sensitivity components, showing that high-sensitivity components are more strongly tied to predictive confidence and classification performance. This provides a principled tool for interpreting black-box deep neural networks in medical imaging.
GMN4AD: New Graph Matching Network Boosts Alzheimer's Diagnosis Accuracy Using Multi-Center MRI Data
A new artificial intelligence method, GMN4AD, leverages graph matching networks and test-time domain adaptation to improve Alzheimer's disease diagnosis from multi-center structural MRI scans. The approach addresses inter-site heterogeneity and achieves superior performance on three public datasets compared to existing methods.
UniBrain: A Unified Multimodal Model for Brain MRI Imputation and Understanding
Researchers propose UniBrain, a unified multimodal large language model for brain MRI analysis that handles missing data through joint imputation and understanding. The model uses interleaved data flow, self-alignment, and dynamic hidden state mechanisms to achieve high performance on multi-disease MRI datasets.
Mutual Distillation of Dual Foundation Models Achieves State-of-the-Art PET/CT Segmentation with Only 5 Labeled Cases
Researchers propose MuDuo, a mutual distillation framework that leverages two foundation models (SAM-Med3D for CT, SegAnyPET for PET) to distill knowledge into a lightweight student network for semi-supervised PET/CT segmentation. Achieving state-of-the-art performance on the AutoPET dataset with only 5 labeled cases, the approach eliminates manual prompts and maximizes unlabeled data utility.
Medical Image Segmentation Survey: U-Net, Transformers, SAM and Clinical Translation Challenges
A new arXiv survey systematically reviews medical image segmentation methods based on U-Net, Transformer, and SAM architectures. It covers public datasets, evaluation metrics, and key challenges, aiming to guide future research and clinical adoption. The authors have made all related resources publicly available on GitHub.
LUCID AI Framework Enhances Sparse-View CT Reconstruction with Flow Matching and Consistency Guidance
Researchers propose LUCID, a sparsity-adaptive consistency-guided framework for sparse-view CT reconstruction that uses flow matching to generate high-quality images from undersampled data. The method reduces radiation dose and scanning time while improving image quality and structural fidelity.
AI Video Generation Method for Cardiac MRI Addresses Data Scarcity with Latent Motion Modeling
Researchers propose a generative method for synthesizing temporally coherent and anatomically consistent cardiac sequences from clinical text prompts. The model decouples spatial structure from temporal motion using a fine-tuned diffusion model and latent flow conditioning, achieving strong fidelity metrics. This approach addresses the scarcity of public cardiac MRI datasets.
GPU-Free AI Model UltraSeg Enables Real-Time Ultrasound Segmentation on CPUs
UltraSeg, an ultra-lightweight AI architecture, enables real-time point-of-care ultrasound segmentation without GPU dependency. Running on single-core CPUs at up to 89.7 FPS, it matches or exceeds larger models like UNet, making AI diagnostics viable in resource-limited settings.
AI-driven Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape Functions from Knee Radiographs
Researchers propose a landmark-free automated workflow using Implicit Neural Shape Functions (INSF) to assess lower-limb alignment from knee radiographs. The method encodes anatomy into a compact latent space and regresses clinical measurements directly, achieving performance comparable to manual methods and state-of-the-art landmark-based approaches. Trained on 566 radiographs and tested on internal and external datasets, the approach offers flexibility for extension to new tasks.
EyeMVP AI Model Enhances Retinal Screening by Learning OCT Insights from Fundus Photos
Researchers developed EyeMVP, a cross-modal retinal foundation model that enriches color fundus photography (CFP) with depth-resolved information from optical coherence tomography (OCT). Pretrained on 674,893 paired images from 112,642 patients across eight Chinese hospitals, EyeMVP outperforms leading models on 16 downstream tasks including macular edema detection (AUROC 0.948 vs 0.852) and myopic macular schisis (0.825).
Deep Learning Automates Doppler Angle Estimation in Ultrasound, Reducing Measurement Errors
A deep learning approach developed using 2100 carotid ultrasound images can automatically estimate Doppler angle, reducing error. The best model achieved mean absolute error less than clinical threshold, potentially improving blood velocity measurements.