Stroke is a leading cause of death and long-term disability worldwide, and early detection remains a significant clinical challenge. This study proposes an Effective Brain Stroke Diagnosis Strategy (EBDS). The hybrid deep learning framework integrates Vision Transformer (ViT) and VGG16 to enable accurate and interpretable stroke detection from CT images. The model was trained and evaluated using a publicly available dataset from Kaggle, achieving impressive results: a test accuracy of 99.6%, a precision of 1.00 for normal cases and 0.98 for stroke cases, a recall of 0.99 for normal cases and 1.00 for stroke cases, and an overall F1-score of 0.99. These results demonstrate the robustness and reliability of the EBDS model, which outperforms several recent state-of-the-art methods. To enhance clinical trust, the model incorporates explainability techniques, such as Grad-CAM and LIME, which provide visual insights into its decision-making process. The EBDS framework is designed for real-time application in emergency settings, offering both high diagnostic performance and interpretability. This work addresses a critical research gap in early brain stroke diagnosis and contributes a scalable, explainable, and clinically relevant solution for medical imaging diagnostics.

Accurate tissue motion tracking is critical to ensure treatment outcome and safety in 2D-Cine MRI-guided radiotherapy. This is typically achieved by registration of sequential images, but existing methods often face challenges with large misalignments and lack of interpretability. In this paper, we introduce DINOMotion, a novel deep learning framework based on DINOv2 with Low-Rank Adaptation (LoRA) layers for robust, efficient, and interpretable motion tracking. DINOMotion automatically detects corresponding landmarks to derive optimal image registration, enhancing interpretability by providing explicit visual correspondences between sequential images. The integration of LoRA layers reduces trainable parameters, improving training efficiency, while DINOv2's powerful feature representations offer robustness against large misalignments. Unlike iterative optimization-based methods, DINOMotion directly computes image registration at test time. Our experiments on volunteer and patient datasets demonstrate its effectiveness in estimating both linear and nonlinear transformations, achieving Dice scores of 92.07% for the kidney, 90.90% for the liver, and 95.23% for the lung, with corresponding Hausdorff distances of 5.47 mm, 8.31 mm, and 6.72 mm, respectively. DINOMotion processes each scan in approximately 30ms and consistently outperforms state-of-the-art methods, particularly in handling large misalignments. These results highlight its potential as a robust and interpretable solution for real-time motion tracking in 2D-Cine MRI-guided radiotherapy.

Sparse-view computed tomography (CT) reduces radiation exposure by subsampling projection views, but conventional reconstruction methods produce severe streak artifacts with undersampled data. While deep-learning-based methods enable single-step artifact suppression, they often produce over-smoothed results under significant sparsity. Though diffusion models improve reconstruction via iterative refinement and generative priors, they require hundreds of sampling steps and struggle with stability in highly sparse regimes. To tackle these concerns, we present the Cross-view Generalized Diffusion Model (CvG-Diff), which reformulates sparse-view CT reconstruction as a generalized diffusion process. Unlike existing diffusion approaches that rely on stochastic Gaussian degradation, CvG-Diff explicitly models image-domain artifacts caused by angular subsampling as a deterministic degradation operator, leveraging correlations across sparse-view CT at different sample rates. To address the inherent artifact propagation and inefficiency of sequential sampling in generalized diffusion model, we introduce two innovations: Error-Propagating Composite Training (EPCT), which facilitates identifying error-prone regions and suppresses propagated artifacts, and Semantic-Prioritized Dual-Phase Sampling (SPDPS), an adaptive strategy that prioritizes semantic correctness before detail refinement. Together, these innovations enable CvG-Diff to achieve high-quality reconstructions with minimal iterations, achieving 38.34 dB PSNR and 0.9518 SSIM for 18-view CT using only \textbf{10} steps on AAPM-LDCT dataset. Extensive experiments demonstrate the superiority of CvG-Diff over state-of-the-art sparse-view CT reconstruction methods. The code is available at https://github.com/xmed-lab/CvG-Diff.

Accurate tissue motion tracking is critical to ensure treatment outcome and safety in 2D-Cine MRI-guided radiotherapy. This is typically achieved by registration of sequential images, but existing methods often face challenges with large misalignments and lack of interpretability. In this paper, we introduce DINOMotion, a novel deep learning framework based on DINOv2 with Low-Rank Adaptation (LoRA) layers for robust, efficient, and interpretable motion tracking. DINOMotion automatically detects corresponding landmarks to derive optimal image registration, enhancing interpretability by providing explicit visual correspondences between sequential images. The integration of LoRA layers reduces trainable parameters, improving training efficiency, while DINOv2's powerful feature representations offer robustness against large misalignments. Unlike iterative optimization-based methods, DINOMotion directly computes image registration at test time. Our experiments on volunteer and patient datasets demonstrate its effectiveness in estimating both linear and nonlinear transformations, achieving Dice scores of 92.07% for the kidney, 90.90% for the liver, and 95.23% for the lung, with corresponding Hausdorff distances of 5.47 mm, 8.31 mm, and 6.72 mm, respectively. DINOMotion processes each scan in approximately 30ms and consistently outperforms state-of-the-art methods, particularly in handling large misalignments. These results highlight its potential as a robust and interpretable solution for real-time motion tracking in 2D-Cine MRI-guided radiotherapy.

This study aimed to develop and validate deep learning models using [¹⁸F]FDG PET/CT to predict PD-L1 status in esophageal cancer (EC) patients. Additionally, we assessed the potential of derived deep learning model scores (DLS) for survival stratification in immunotherapy. In this retrospective study, we included 331 EC patients from two centers, dividing them into training, internal validation, and external validation cohorts. Fifty patients who received immunotherapy were followed up. We developed four 3D ResNet10-based models-PET + CT + clinical factors (CPC), PET + CT (PC), PET (P), and CT (C)-using pre-treatment [¹⁸F]FDG PET/CT scans. For comparison, we also constructed a logistic model incorporating clinical factors (clinical model). The DLS were evaluated as radiological markers for survival stratification, and nomograms for predicting survival were constructed. The models demonstrated accurate prediction of PD-L1 status. The areas under the curve (AUCs) for predicting PD-L1 status were as follows: CPC (0.927), PC (0.904), P (0.886), C (0.934), and the clinical model (0.603) in the training cohort; CPC (0.882), PC (0.848), P (0.770), C (0.745), and the clinical model (0.524) in the internal validation cohort; and CPC (0.843), PC (0.806), P (0.759), C (0.667), and the clinical model (0.671) in the external validation cohort. The CPC and PC models exhibited superior predictive performance. Survival analysis revealed that the DLS from most models effectively stratified overall survival and progression-free survival at appropriate cut-off points (P < 0.05), outperforming stratification based on PD-L1 status (combined positive score ≥ 10). Furthermore, incorporating model scores with clinical factors in nomograms enhanced the predictive probability of survival after immunotherapy. Deep learning models based on [¹⁸F]FDG PET/CT can accurately predict PD-L1 status in esophageal cancer patients. The derived DLS can effectively stratify survival outcomes following immunotherapy, particularly when combined with clinical factors.

Thyroid cancer is one of the most common endocrine malignancies. Its incidence has steadily increased in recent years. Distinguishing between benign and malignant thyroid nodules (TNs) is challenging due to their overlapping imaging features. The rapid advancement of artificial intelligence (AI) in medical image analysis, particularly deep learning (DL) algorithms, has provided novel solutions for automated TN detection. However, existing studies exhibit substantial heterogeneity in diagnostic performance. Furthermore, no systematic evidence-based research comprehensively assesses the diagnostic performance of DL models in this field. This study aimed to execute a systematic review and meta-analysis to appraise the performance of DL algorithms in diagnosing TN malignancy, identify key factors influencing their diagnostic efficacy, and compare their accuracy with that of clinicians in image-based diagnosis. We systematically searched multiple databases, including PubMed, Cochrane, Embase, Web of Science, and IEEE, and identified 41 eligible studies for systematic review and meta-analysis. Based on the task type, studies were categorized into segmentation (n=14) and detection (n=27) tasks. The pooled sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were calculated for each group. Subgroup analyses were performed to examine the impact of transfer learning and compare model performance against clinicians. For segmentation tasks, the pooled sensitivity, specificity, and AUC were 82% (95% CI 79%-84%), 95% (95% CI 92%-96%), and 0.91 (95% CI 0.89-0.94), respectively. For detection tasks, the pooled sensitivity, specificity, and AUC were 91% (95% CI 89%-93%), 89% (95% CI 86%-91%), and 0.96 (95% CI 0.93-0.97), respectively. Some studies demonstrated that DL models could achieve diagnostic performance comparable with, or even exceeding, that of clinicians in certain scenarios. The application of transfer learning contributed to improved model performance. DL algorithms exhibit promising diagnostic accuracy in TN imaging, highlighting their potential as auxiliary diagnostic tools. However, current studies are limited by suboptimal methodological design, inconsistent image quality across datasets, and insufficient external validation, which may introduce bias. Future research should enhance methodological standardization, improve model interpretability, and promote transparent reporting to facilitate the sustainable clinical translation of DL-based solutions.

Purpose: Although elevated BMI is a well-known risk factor for type 2 diabetes, the disease's presence in some lean adults and absence in others with obesity suggests that detailed body composition may uncover abdominal phenotypes of type 2 diabetes. With AI, we can now extract detailed measurements of size, shape, and fat content from abdominal structures in 3D clinical imaging at scale. This creates an opportunity to empirically define body composition signatures linked to type 2 diabetes risk and protection using large-scale clinical data. Approach: To uncover BMI-specific diabetic abdominal patterns from clinical CT, we applied our design four times: once on the full cohort (n = 1,728) and once on lean (n = 497), overweight (n = 611), and obese (n = 620) subgroups separately. Briefly, our experimental design transforms abdominal scans into collections of explainable measurements through segmentation, classifies type 2 diabetes through a cross-validated random forest, measures how features contribute to model-estimated risk or protection through SHAP analysis, groups scans by shared model decision patterns (clustering from SHAP) and links back to anatomical differences (classification). Results: The random-forests achieved mean AUCs of 0.72-0.74. There were shared type 2 diabetes signatures in each group; fatty skeletal muscle, older age, greater visceral and subcutaneous fat, and a smaller or fat-laden pancreas. Univariate logistic regression confirmed the direction of 14-18 of the top 20 predictors within each subgroup (p < 0.05). Conclusions: Our findings suggest that abdominal drivers of type 2 diabetes may be consistent across weight classes.

Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain's white matter. Among machine learning approaches, reinforcement learning (RL) has emerged as a promising framework for tractography, outperforming traditional methods in several key aspects. TractOracle-RL, a recent RL-based approach, reduces false positives by incorporating anatomical priors into the training process via a reward-based mechanism. In this paper, we investigate four extensions of the original TractOracle-RL framework by integrating recent advances in RL, and we evaluate their performance across five diverse diffusion MRI datasets. Results demonstrate that combining an oracle with the RL framework consistently leads to robust and reliable tractography, regardless of the specific method or dataset used. We also introduce a novel RL training scheme called Iterative Reward Training (IRT), inspired by the Reinforcement Learning from Human Feedback (RLHF) paradigm. Instead of relying on human input, IRT leverages bundle filtering methods to iteratively refine the oracle's guidance throughout training. Experimental results show that RL methods trained with oracle feedback significantly outperform widely used tractography techniques in terms of accuracy and anatomical validity.

Acute myocardial infarction (MI) alters cardiomyocyte geometry and architecture, leading to changes in the acoustic properties of the myocardium. This study examines ultrasomics-a novel cardiac ultrasound-based radiomics technique to extract high-throughput pixel-level information from images-for identifying ultrasonic texture and morphologic changes associated with infarcted myocardium. We included 684 participants from multisource data: a) a retrospective single-center matched case-control dataset, b) a prospective multicenter matched clinical trial dataset, and c) an open-source international and multivendor dataset. Handcrafted and deep transfer learning-based ultrasomics features from 2- and 4-chamber echocardiographic views were used to train machine learning (ML) models with the use of leave-one-source-out cross-validation for external validation. The ML model showed a higher AUC than transfer learning-based deep features in identifying MI [AUCs: 0.87 [95% CI: 0.84-0.89] vs 0.74 [95% CI: 0.70-0.77]; P < 0.0001]. ML probability was an independent predictor of MI even after adjusting for conventional echocardiographic parameters [adjusted OR: 1.03 [95% CI: 1.01-1.05]; P < 0.0001]. ML probability showed diagnostic value in differentiating acute MI, even in the presence of myocardial dysfunction (averaged longitudinal strain [LS] <16%) (AUC: 0.84 [95% CI: 0.77-0.89]). In addition, combining averaged LS with ML probability significantly improved predictive performance compared with LS alone (AUCs: 0.86 [95% CI: 0.80-0.91] vs 0.80 [95% CI: 0.72-0.87]; P = 0.02). Visualization of ultrasomics features with the use of a Manhattan plot discriminated infarcted and noninfarcted segments (P < 0.001) and facilitated parametric visualization of infarcted myocardium. This study demonstrates the potential of cardiac ultrasomics to distinguish healthy from infarcted myocardium and highlights the need for validation in diverse populations to define its role and incremental value in myocardial tissue characterization beyond conventional echocardiography.

Accurate detection and segmentation of lesions induced by High-Intensity Focused Ultrasound (HIFU) in medical imaging remain significant challenges in automated disease diagnosis. Traditional methods heavily rely on labeled data, which is often scarce, expensive, and time-consuming to obtain. Moreover, existing approaches frequently struggle with variations in medical data and the limited availability of annotated datasets, leading to suboptimal performance. To address these challenges, this paper introduces an innovative framework called the Optimized Multi-Task Contrastive Learning Framework (OMCLF), which leverages self-supervised learning (SSL) and genetic algorithms (GA) to enhance HIFU lesion detection and segmentation. OMCLF integrates classification and segmentation into a unified model, utilizing a shared backbone to extract common features. The framework systematically optimizes feature representations, hyperparameters, and data augmentation strategies tailored for medical imaging, ensuring that critical information, such as lesion details, is preserved. By employing a genetic algorithm, OMCLF explores and optimizes augmentation techniques suitable for medical data, avoiding distortions that could compromise diagnostic accuracy. Experimental results demonstrate that OMCLF outperforms single-task methods in both classification and segmentation tasks while significantly reducing dependency on labeled data. Specifically, OMCLF achieves an accuracy of 93.3% in lesion detection and a Dice score of 92.5% in segmentation, surpassing state-of-the-art methods such as SimCLR and MoCo. The proposed approach achieves superior accuracy in identifying and delineating HIFU-induced lesions, marking a substantial advancement in medical image interpretation and automated diagnosis. OMCLF represents a significant step forward in the evolutionary optimization of self-supervised learning, with potential applications across various medical imaging domains.