• Developed FPQuant, a multi-task deep learning framework for fingerprint phenotyping. • Achieved 97.18% accuracy in six fingerprint pattern classifications. • Demonstrated 98.63% precision in singularity detection with optimized localization. • Identified unreported geographic variations in fingerprint traits. Fingerprint morphology, while evolutionary conserved yet individually distinct, emerges as a pivotal biometric identifier in anthropological research and forensic investigation. Current methodologies for precise identification and quantification of complex morphological features—particularly ridge counting and mean ridge-furrow pairs ridge breadth—remain constrained by labor-intensive and monolithic pattern recognition systems. This study presents FPQuant (Fingerprint Phenomics Quantification), a multi-task deep learning framework integrating the most comprehensive fingerprint pattern classification, singularity detection, and quantification of 12 morphometric phenotypes to date. Leveraging NSPT database of 28,867 expert-curated fingerprints, FPQuant achieved state-of-the-art performance with 97.18% (6-class), 98.62% (5-class), and 98.67% (4-class) pattern classification accuracy; 98.63% precision in topological singularity detection through optimized discrete keypoint localization; and expert-level precision in critical quantitative measurements including ridge counting. Cross-database validation demonstrated extraordinary generalizability with 96.20% of 5-class accuracy on NIST-4 and 97.75% of singularity precision on FVC2002 DB1. Notably, FPQuant’s integrated phenotypic capability revealed uncharacterized geographic variation in six morphometric traits, establishing novel fingerprint morphometric biomarkers for anthropological research. This study creates a scalable technical paradigm that bridging fingerprint phenomics with large-scale population study, while providing potential new research avenues across anthropology, forensics and biometric authentication.

White matter hyperintensities (WMH) are important imaging biomarkers for cerebral small vessel disease, and their automatic segmentation across data with different distributions is crucial for assessing brain health and supporting diagnosis. However, cross-domain WMH segmentation remains challenging in privacy-sensitive and label-scarce clinical settings. Existing methods either relied on source domain data, violating privacy constraints, or lacked spatial guidance, which resulted in poor generalization, such as low sensitivity to small lesions. To address these challenges, we developed a source-free domain adaptation (SFDA) framework enhanced by federated spatial prior modeling. Our method used a dual-path pseudo-label generator that leveraged spatial priors to improve boundary accuracy and enhance the detection of small lesions. These priors were optimized via federated learning across multiple sites without sharing raw data, boosting model generalization while preserving privacy. The model was then fine-tuned using refined pseudo-labels. Experimental results demonstrated that our method consistently outperforms state-of-the-art UDA and SFDA methods, achieving 3-10% DSC improvement in most sites across 3 public and 7 private datasets. It also showed superior performance in small lesion detection and boundary delineation. Our method offered a robust, privacy-preserving solution for WMH segmentation and provided valuable support for early diagnosis and risk assessment of cerebrovascular diseases.

Personalized healthcare increasingly relies on AI-driven multimodal fusion to enhance diagnostic precision and treatment planning. However, long MRI acquisition times, imaging artifacts, and missing modalities often lead to incomplete critical imaging information, limiting the application of multimodal MRI in personalized diagnostics. To address this challenge, we propose Dual-Scale Multimodal Fusion Network (Dual-MFNet), a novel AI-driven approach to personalized MRI synthesis for reconstructing missing modalities with high anatomical fidelity. Our method leverages state-space models to capture long-range contextual dependencies while preserving local structural integrity, ensuring accurate cross-modal synthesis. The Dual-Scale Feature Fuser (Dual-Fuser) balances global coherence with fine-grained detail preservation, while the Twin-Stream Fusion module (TSF) dynamically enhances critical cross-modal information. In addition, the Feature Aggregation (FA) module consolidates multimodal input into a cohesive representation, producing high-fidelity synthesized MRI customized to individual patient needs. To assess clinical relevance, we conducted extensive quantitative evaluations and a radiological reader study with five experienced radiologists. The results demonstrate that Dual-MFNet outperforms state-of-the-art methods, particularly in preserving tumor boundaries, fine tissue textures, and anatomical clarity, making it a valuable tool to advance personalized MRI-based diagnostics.

Predicting stroke outcome remains challenging due to inherent heterogeneity, misalignment of multimodal clinical data, and the availability of well-annotated longitudinal datasets. Current methodologies often lack robustness and generalizability across these tasks. We propose a few-shot contrastive learning framework that integrates brain MRI images and structured clinical records for cross-task prognosis prediction, addressing both morphological and functional outcomes. Our method combines Model-Agnostic Meta-Learning (MAML) with a two-step contrastive learning strategy including self-awareness learning that captures task-specific features and domain learning that facilitates cross-dataset generalization. To handle inconsistencies in tabular data, a Misalignment Separation technique was adopted. The framework jointly trains a domain encoder on multimodal inputs, capturing shared and task-specific prior knowledge to enhance predictive robustness. Evaluations on 309 patients for morphological outcome and 341 patients for functional outcome, as well as on external validation datasets, demonstrated that our approach outperformed SimCLR and conventional supervised methods, and could effectively integrate cross-task datasets. This framework highlights the potential of multimodal few-shot learning for robust stroke prognosis prediction for small-sample datasets.

Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the clinical reference standard for diagnosing cardiovascular disease. However, its adoption is hindered by long scan times, complex contrasts, and inconsistent quality. While deep learning methods perform well on specific CMR imaging sequences, they often fail to generalize across modalities and sampling schemes. The lack of benchmarks for high-quality, fast CMR image reconstruction further limits technology comparison and adoption. The CMRxRecon2024 challenge, attracting over 200 teams from 18 countries, addressed these issues with two tasks: generalization to unseen modalities and robustness to diverse undersampling patterns. We introduced the largest public multi-modality CMR raw dataset, an open benchmarking platform, and shared code. Analysis of the best-performing solutions revealed that prompt-based adaptation and enhanced physics-driven consistency enabled strong cross-scenario performance. These findings establish principles for generalizable reconstruction models and advance clinically translatable AI in cardiovascular imaging.

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown superiority in learning visual representation, which combines the advantages of linear scalability and global sensitivity. In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation. A novel Arbitrary Scan Masking (ASM) mechanism "masks out" redundant information to introduce randomness for further uncertainty estimation. Compared to the commonly used Monte Carlo (MC) dropout, our proposed MC-ASM provides an uncertainty map without the need for hyperparameter tuning and mitigates the performance drop typically observed when applying dropout to low-level tasks. For further texture preservation and better perceptual quality, we employ the wavelet transformation into MambaMIR and explore its variant based on the Generative Adversarial Network, namely MambaMIR-GAN. Comprehensive experiments have been conducted for multiple representative medical image reconstruction tasks, demonstrating that the proposed MambaMIR and MambaMIR-GAN outperform other baseline and state-of-the-art methods in different reconstruction tasks, where MambaMIR achieves the best reconstruction fidelity and MambaMIR-GAN has the best perceptual quality. In addition, our MC-ASM provides uncertainty maps as an additional tool for clinicians, while mitigating the typical performance drop caused by the commonly used dropout.

During labor, transperineal ultrasound imaging can acquire real-time midsagittal images, through which the pubic symphysis and fetal head can be accurately identified, and the angle of progression (AoP) between them can be calculated, thereby quantitatively evaluating the descent and position of the fetal head in the birth canal in real time. However, current segmentation methods based on convolutional neural networks (CNNs) and Transformers generally depend heavily on large-scale manually annotated data, which limits their adoption in practical applications. In light of this limitation, this paper develops a new Transformer-based Semi-supervised Segmentation Network (TransSeg). This method employs a Vision Transformer as the backbone network and introduces a Channel-wise Cross Attention (CCA) mechanism to effectively reconstruct the features of unlabeled samples into the labeled feature space, promoting architectural innovation in semi-supervised segmentation and eliminating the need for complex training strategies. In addition, we design a Semantic Information Storage (S-InfoStore) module and a Channel Semantic Update (CSU) strategy to dynamically store and update feature representations of unlabeled samples, thereby continuously enhancing their expressiveness in the feature space and significantly improving the model's utilization of unlabeled data. We conduct a systematic evaluation of the proposed method on the FH-PS-AoP dataset. Experimental results demonstrate that TransSeg outperforms existing mainstream methods across all evaluation metrics, verifying its effectiveness and advancement in semi-supervised semantic segmentation tasks.

Time-of-flight magnetic resonance angiography (TOF-MRA) is a prevalent vascular imaging technique for assessing cerebrovascular diseases. Compared to routine 3T TOF-MRA, 7T TOF-MRA provides vascular structures with a higher signal-to-noise ratio (SNR) and better vessel contrast, revealing greater vascular details. However, the inaccessibility of 7T scanners and specific physiological and technical concerns limit its clinical application. Therefore, we aimed to generate high-quality 7T-like TOF-MRA from 3T TOF-MRA. Considering the spatial sparsity of vessel signals, the visibility discrepancy of distal and small vessels between 3T and 7T images, and the subtle spatial misalignment between paired data, we proposed a novel aleatoric-uncertainty-aware maximum intensity projection-based generative adversarial network (AU-MIPGAN). In our method, we employed a knowledge distillation (KD) framework to incorporate multi-directional MIP information into the 3T-to-7T learning process to strengthen the learning of vessels and provide three-dimensional (3D) vascular morphological knowledge for the student model, facilitating accurate generation of vascular structures. Furthermore, we exploited AU modeling to compensate for the spatial misalignment between paired 3T and 7T images during the training procedure, which helped the model concentrate more on learning the intrinsic gap between 3T and 7T images. Qualitative and quantitative results demonstrated that the proposed AU-MIPGAN can achieve promising performance for 7T-like TOF-MRA generation.

Magnetic resonance imaging (MRI) possesses the unique versatility to acquire images under a diverse array of distinct tissue contrasts, which makes multicontrast super-resolution (SR) techniques possible and needful. Compared with single-contrast MRI SR, multicontrast SR is expected to produce higher quality images by exploiting a variety of complementary information embedded in different imaging contrasts. However, existing approaches still have two shortcomings: 1) most of them are convolution-based methods and, hence, weak in capturing long-range dependencies, which are essential for MR images with complicated anatomical patterns and 2) they ignore to make full use of the multicontrast features at different scales and lack effective modules to match and aggregate these features for faithful SR. To address these issues, we develop a novel multicontrast MRI SR network via transformer-empowered multiscale feature matching and aggregation, dubbed McMRSR$^{++}$. First, we tame transformers to model long-range dependencies in both reference and target images at different scales. Then, a novel multiscale feature matching and aggregation method is proposed to transfer corresponding contexts from reference features at different scales to the target features and interactively aggregate them Furthermore, a texture-preserving branch and a contrastive constraint are incorporated into our framework for enhancing the textural details in the SR images. Experimental results on both public and clinical in vivo datasets show that McMRSR$^{++}$outperforms state-of-the-art methods under peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), and root mean square error (RMSE) metrics significantly. Visual results demonstrate the superiority of our method in restoring structures, demonstrating its great potential to improve scan efficiency in clinical practice.

Magnetic resonance imaging (MRI) can present multicontrast images of the same anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared with SR reconstruction using a single-contrast, multicontrast SR reconstruction is promising to yield SR images with higher quality by leveraging diverse yet complementary information embedded in different imaging modalities. However, existing methods still have two shortcomings: (1) they neglect that the multi-contrast features at different scales contain different anatomical details and hence lack effective mechanisms to match and fuse these features for better reconstruction; and (2) they are still deficient in capturing long-range dependencies, which are essential for the regions with complicated anatomical structures. We propose a novel network to comprehensively address these problems by developing a set of innovative Transformer-empowered multi-scale contextual matching and aggregation techniques; we call it McMRSR. Firstly, we tame transformers to model long-range dependencies in both reference and target images. Then, a new multi-scale contextual matching method is proposed to capture corresponding contexts from reference features at different scales. Furthermore, we introduce a multi-scale aggregation mechanism to gradually and interactively aggregate multi-scale matched features for reconstructing the target SR MR image. Extensive experiments demonstrate that our network outperforms state-of-the-art approaches and has great potential to be applied in clinical practice. Codes are available at https://github.com/XAIMI-Lab/McMRSR.

Detecting out-of-distribution (OOD) samples is essential for reliably deploying deep learning classifiers in open-world applications. However, existing detectors relying on discriminative probability suffer from the overconfident posterior estimate for OOD data. Other reported approaches either impose strong unproven parametric assumptions to estimate OOD sample density or develop empirical detectors lacking clear theoretical motivations. To address these issues, we propose a theoretical probabilistic framework for OOD detection in deep classification networks, in which two regularization constraints are constructed to reliably calibrate and estimate sample density to identify OOD. Specifically, the density consistency regularization enforces the agreement between analytical and empirical densities of observable low-dimensional categorical labels. The contrastive distribution regularization separates the densities between in distribution (ID) and distribution-deviated samples. A simple and robust implementation algorithm is also provided, which can be used for any pre-trained neural network classifiers. To the best of our knowledge, we have conducted the most extensive evaluations and comparisons on computer vision benchmarks. The results show that our method significantly outperforms state-of-the-art detectors, and even achieves comparable or better performance than methods utilizing additional large-scale outlier exposure datasets.

Single-Shot Echo Planar Imaging (SSEPI) based Diffusion Weighted Imaging (DWI) has shortcomings such as low resolution and severe distortions. In contrast, Multi-Shot EPI (MSEPI) provides optimal spatial resolution but increases scan time. This study proposed a Multiple b-value mOdel-based Residual Network (MORN) model to reconstruct multiple b-value high-resolution DWI from undersampled k-space data simultaneously. We incorporated Parallel Imaging (PI) into a residual U-net to reconstruct multiple b-value multi-coil data with the supervision of MUltiplexed Sensitivity-Encoding (MUSE) reconstructed Multi-Shot DWI (MSDWI). Moreover, asymmetric concatenations among different b-values and the combined loss to back propagate helped the feature transfer. After training and validation of the MORN in a dataset of 32 healthy cases, additional assessments were performed on 6 patients with different tumor types. The experimental results demonstrated that the MORN model outperformed conventional PI reconstruction (i.e. SENSE) and two state-of-the-art deep learning methods (SENSE-GAN and VSNet) in terms of PSNR (Peak Signal-to-Noise Ratio), SSIM (Structual SIMilarity) and apparent diffusion coefficient maps. In addition, using the pre-trained model under DWI, the MORN achieved consistent fractional anisotrophy and mean diffusivity reconstructed from multiple diffusion directions. Hence, the proposed method shows potential in clinical application according to the observations on tumor patients as well as images of multiple diffusion directions.

OBJECTIVE: Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a 4-dimensional (4D) deep learning model based on 3D convolution and convolutional long short-term memory (C-LSTM) for hepatocellular carcinoma (HCC) lesion segmentation. METHODS: The proposed deep learning model utilizes 4D information on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) images to assist liver tumor segmentation. Specifically, a shallow U-net based 3D CNN module was designed to extract 3D spatial domain features from each DCE phase, followed by a 4-layer C-LSTM network module for time domain information exploitation. The combined information of multi-phase DCE images and the manner by which tissue imaging features change on multi-contrast images allow the network to more effectively learn the characteristics of HCC, resulting in better segmentation performance. RESULTS: The proposed model achieved a Dice score of 0.825± 0.077, a Hausdorff distance of 12.84± 8.14 mm, and a volume similarity of 0.891± 0.080 for liver tumor segmentation, which outperformed the 3D U-net model, RA-UNet model and other models in the ablation study in both internal and external test sets. Moreover, the performance of the proposed model is comparable to the nnU-Net model, which showed state-of-the-art performance in many segmentation tasks, with significantly reduced prediction time. CONCLUSION: The proposed 3D convolution and C-LSTM based model can achieve accurate segmentation of HCC lesions.

The bilateral filter method is a nonlinear filter with spatial averaging without smoothing edges. It has shown to be an effective image denoising technique. Denoising performance using the bilateral filter is affected by the filter parameters, which are image dependent and require experimental trials. We propose an automatic and effective PSO-based method of parameters selection for the bilateral filter in image denoising. Intensity domain parameter δr and the radius parameter d are optimized by the PSO algorithm, in which SSIM (structural similarity index) is employed in fitness function. We firstly compare our approach with other four classical filtering methods at different types and levels of noise. We also compare the denoising performance with different values of the parameter δd. Experimental results on three sets of color images have shown that the proposed method of parameter selection outperformed the other filtering methods in denoising standard test images corrupted by different types and levels of noise.