This study aims to develop and validate a multi-modal machine learning model for preoperative risk stratification in papillary thyroid carcinoma (PTC), addressing limitations of current systems that rely on postoperative pathological features. We analyzed 974 PTC patients from three medical centers in China using a multi-modal approach integrating: (1) clinical indicators, (2) immunological indices, (3) ultrasound radiomics features, and (4) CT radiomics features. Our methodology employed gradient boosting machine for feature selection and random forest for classification, with model interpretability provided through SHapley Additive exPlanations (SHAP) analysis. The model was validated on internal (n = 225) and two external cohorts (n = 51, n = 174). The final 15-feature model achieved AUCs of 0.91, 0.84, and 0.77 across validation cohorts, improving to 0.96, 0.95, and 0.89 after cohort-specific refitting. SHAP analysis revealed CT texture features, ultrasound morphological features, and immune-inflammatory markers as key predictors, with consistent patterns across validation sites despite center-specific variations. Subgroup analysis showed superior performance in tumors > 1 cm and patients without extrathyroidal extension. Our multi-modal machine learning approach provides accurate preoperative risk stratification for PTC with robust cross-center applicability. This computational framework for integrating heterogeneous imaging and clinical data demonstrates the potential of multi-modal joint learning in healthcare imaging to transform clinical decision-making by enabling personalized treatment planning.

Content-based mammographic image retrieval systems require exact BIRADS categorical matching across five distinct classes, presenting significantly greater complexity than binary classification tasks commonly addressed in literature. Current medical image retrieval studies suffer from methodological limitations including inadequate sample sizes, improper data splitting, and insufficient statistical validation that hinder clinical translation. We developed a comprehensive evaluation framework systematically comparing CNN architectures (DenseNet121, ResNet50, VGG16) with advanced training strategies including sophisticated fine-tuning, metric learning, and super-ensemble optimization. Our evaluation employed rigorous stratified data splitting (50%/20%/30% train/validation/test), 602 test queries, and systematic validation using bootstrap confidence intervals with 1,000 samples. Advanced fine-tuning with differential learning rates achieved substantial improvements: DenseNet121 (34.79% precision@10, 19.64% improvement) and ResNet50 (34.54%, 19.58% improvement). Super-ensemble optimization combining complementary architectures achieved 36.33% precision@10 (95% CI: [34.78%, 37.88%]), representing 24.93% improvement over baseline and providing 3.6 relevant cases per query. Statistical analysis revealed significant performance differences between optimization strategies (p<0.001) with large effect sizes (Cohen's d>0.8), while maintaining practical search efficiency (2.8milliseconds). Performance significantly exceeds realistic expectations for 5-class medical retrieval tasks, where literature suggests 20-25% precision@10 represents achievable performance for exact BIRADS matching. Our framework establishes new performance benchmarks while providing evidence-based architecture selection guidelines for clinical deployment in diagnostic support and quality assurance applications.

Interstitial lung disease (ILD) prognostication incorporates clinical history, pulmonary function testing (PFTs), and chest CT pattern classifications. The machine learning classifier, Fibresolve, includes a model to help detect CT patterns associated with idiopathic pulmonary fibrosis (IPF). We developed and tested new Fibresolve software to predict outcomes in patients with ILD. Fibresolve uses a transformer (ViT) algorithm to analyze CT imaging that additionally embeds PFTs, age, and sex to produce an overall risk score. The model was trained to optimize risk score in a dataset of 602 subjects designed to maximize predictive performance via Cox proportional hazards. Validation was completed with the first hazard ratio assessment dataset, then tested in a second datatest set. 61 % of 220 subjects died in the validation set's study period, whereas 40 % of the 407 subjects died in the second dataset's. The validation dataset's mortality hazard ratio (HR) was 3.66 (95 % CI: 2.09-6.42) and 4.66 (CI: 2.47-8.77) for the moderate and high-risk groups. In the second dataset, Fibresolve was a predictor of mortality at initial visit, with a HR of 2.79 (1.73-4.49) and 5.82 (3.53-9.60) in the moderate and high-risk groups. Similar predictive performance was seen at follow-up visits, as well as with changes in the Fibresolve scores over sequential visits. Fibresolve predicts mortality by automatically assessing combined CT, PFTs, age, and sex into a ViT model. The new software algorithm affords accurate prognostication and demonstrates the ability to detect clinical changes over time.

Magnetic Resonance Imaging (MRI) is widely utilized in medical imaging due to its high resolution and non-invasive nature. However, the prolonged acquisition time significantly limits its clinical applicability. Although traditional compressed sensing (CS) techniques can accelerate MRI acquisition, they often lead to degraded reconstruction quality under high undersampling rates. Deep learning-based methods, including CNN- and GAN-based approaches, have improved reconstruction performance, yet are limited by their local receptive fields, making it challenging to effectively capture long-range dependencies. Moreover, these models typically exhibit high computational complexity, which hinders their efficient deployment in practical scenarios. To address these challenges, we propose a lightweight Multi-scale Context-Aware Generative Adversarial Network (MCA-GAN), which enhances MRI reconstruction through dual-domain generators that collaboratively optimize both k-space and image-domain representations. MCA-GAN integrates several lightweight modules, including Depthwise Separable Local Attention (DWLA) for efficient local feature extraction, Adaptive Group Rearrangement Block (AGRB) for dynamic inter-group feature optimization, Multi-Scale Spatial Context Modulation Bridge (MSCMB) for multi-scale feature fusion in skip connections, and Channel-Spatial Multi-Scale Self-Attention (CSMS) for improved global context modeling. Extensive experiments conducted on the IXI, MICCAI 2013, and MRNet knee datasets demonstrate that MCA-GAN consistently outperforms existing methods in terms of PSNR and SSIM. Compared to SepGAN, the latest lightweight model, MCA-GAN achieves a 27.3% reduction in parameter size and a 19.6% reduction in computational complexity, while attaining the shortest reconstruction time among all compared methods. Furthermore, MCA-GAN exhibits robust performance across various undersampling masks and acceleration rates. Cross-dataset generalization experiments further confirm its ability to maintain competitive reconstruction quality, underscoring its strong generalization potential. Overall, MCA-GAN improves MRI reconstruction quality while significantly reducing computational cost through a lightweight architecture and multi-scale feature fusion, offering an efficient and accurate solution for accelerated MRI.

Dynamic Positron Emission Tomography (PET) scans offer rich spatiotemporal data for detecting malignancies, but their high-dimensionality and noise pose significant challenges. We introduce a novel framework, the Equivariant Spatiotemporal Transformer with MDL-Guided Feature Selection (EST-MDL), which integrates group-theoretic symmetries, Kolmogorov complexity, and Minimum Description Length (MDL) principles. By enforcing spatial and temporal symmetries (e.g., translations and rotations) and leveraging MDL for robust feature selection, our model achieves improved generalization and interpretability. Evaluated on three realworld PET datasets--LUNG-PET, BRAIN-PET, and BREAST-PET--our approach achieves AUCs of 0.94, 0.92, and 0.95, respectively, outperforming CNNs, Vision Transformers (ViTs), and Graph Neural Networks (GNNs) in AUC, sensitivity, specificity, and computational efficiency. This framework offers a robust, interpretable solution for malignancy detection in clinical settings.

Accurate localization of the epileptogenic zone (EZ) is essential for surgical success in temporal lobe epilepsy. While stereoelectroencephalography (SEEG) and structural magnetic resonance imaging (MRI) provide complementary insights, existing unimodal methods fail to fully capture epileptogenic brain activity, and multimodal fusion remains challenging due to data complexity and surgeon-dependent interpretations. To address these issues, we proposed a novel multimodal framework to improve EZ localization with SEEG-drived electrophysiology with structural connectivity in temporal lobe epilepsy. By retrospectively analyzing SEEG, post-implant Computed Tomography (CT) and MRI (T1 & Diffusion Tensor Imaging (DTI)) data from 15 patients, we reconstructed SEEG electrode positions and obtained the SEEG and structural connectivity fusion features. We then proposed a spatiotemporal co-attention deep neural network (ST-CANet) to identify the fusion features, categorizing electrodes into seizure onset zone (SOZ), propagation zone (PZ), and non-involved zone (NIZ). Anatomical EZ boundaries were delineated by fusing the electrode position and classification information on brain atlas. The proposed method was evaluated based on the identification and localization performance of three epilepsy-related zones. The experiment results demonstrate that our method achieves 98.08% average accuracy and outperforms other identification methods, and improves the localization with Dice similarity coefficients (DSC) of 95.65% (SOZ), 92.13% (PZ), and 99.61% (NIZ), aligning with clinically validated surgical resection areas. This multimodal fusion strategy based on electrophysiological and structural connectivity information promises to assist neurosurgeons in accurately localizing EZ and may find broader applications in preoperative planning for epilepsy surgeries.

Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model performance under a limited annotation budget in medical image analysis. Most existing CSAL methods rely on Self-Supervised Learning (SSL) on the target dataset for feature extraction, which is inefficient and limited by insufficient feature representation. Recently, pre-trained Foundation Models (FMs) have shown powerful feature extraction ability with a potential for better CSAL. However, this paradigm has been rarely investigated, with a lack of benchmarks for comparison of FMs in CSAL tasks. To this end, we propose MedCAL-Bench, the first systematic FM-based CSAL benchmark for medical image analysis. We evaluate 14 FMs and 7 CSAL strategies across 7 datasets under different annotation budgets, covering classification and segmentation tasks from diverse medical modalities. It is also the first CSAL benchmark that evaluates both the feature extraction and sample selection stages. Our experimental results reveal that: 1) Most FMs are effective feature extractors for CSAL, with DINO family performing the best in segmentation; 2) The performance differences of these FMs are large in segmentation tasks, while small for classification; 3) Different sample selection strategies should be considered in CSAL on different datasets, with Active Learning by Processing Surprisal (ALPS) performing the best in segmentation while RepDiv leading for classification. The code is available at https://github.com/HiLab-git/MedCAL-Bench.

Radiologists rely on anatomical understanding to accurately delineate pathologies, yet most current deep learning approaches use pure pattern recognition and ignore the anatomical context in which pathologies develop. To narrow this gap, we introduce GRASP (Guided Representation Alignment for the Segmentation of Pathologies), a modular plug-and-play framework that enhances pathology segmentation models by leveraging existing anatomy segmentation models through pseudolabel integration and feature alignment. Unlike previous approaches that obtain anatomical knowledge via auxiliary training, GRASP integrates into standard pathology optimization regimes without retraining anatomical components. We evaluate GRASP on two PET/CT datasets, conduct systematic ablation studies, and investigate the framework's inner workings. We find that GRASP consistently achieves top rankings across multiple evaluation metrics and diverse architectures. The framework's dual anatomy injection strategy, combining anatomical pseudo-labels as input channels with transformer-guided anatomical feature fusion, effectively incorporates anatomical context.

Point clouds have become an increasingly important representation for 3D medical imaging, offering a compact, surface-preserving alternative to traditional voxel or mesh-based approaches. Recent advances in deep learning have enabled rapid progress in extracting, modeling, and analyzing anatomical shapes directly from point cloud data. This paper provides a comprehensive and systematic survey of learning-based shape analysis for medical point clouds, focusing on three fundamental tasks: registration, reconstruction, and variation modeling. We review recent literature from 2021 to 2025, summarize representative methods, datasets, and evaluation metrics, and highlight clinical applications and unique challenges in the medical domain. Key trends include the integration of hybrid representations, large-scale self-supervised models, and generative techniques. We also discuss current limitations, such as data scarcity, inter-patient variability, and the need for interpretable and robust solutions for clinical deployment. Finally, future directions are outlined for advancing point cloud-based shape learning in medical imaging.

Multimodal medical image fusion integrates complementary information from different imaging modalities to enhance diagnostic accuracy and treatment planning. While deep learning methods have advanced performance, existing approaches face critical limitations: Convolutional Neural Networks (CNNs) excel at local feature extraction but struggle to model global context effectively, while Transformers achieve superior long-range modeling at the cost of quadratic computational complexity, limiting clinical deployment. Recent State Space Models (SSMs) offer a promising alternative, enabling efficient long-range dependency modeling in linear time through selective scan mechanisms. Despite these advances, the extension to 3D volumetric data and the clinical validation of fused images remains underexplored. In this work, we propose ClinicalFMamba, a novel end-to-end CNN-Mamba hybrid architecture that synergistically combines local and global feature modeling for 2D and 3D images. We further design a tri-plane scanning strategy for effectively learning volumetric dependencies in 3D images. Comprehensive evaluations on three datasets demonstrate the superior fusion performance across multiple quantitative metrics while achieving real-time fusion. We further validate the clinical utility of our approach on downstream 2D/3D brain tumor classification tasks, achieving superior performance over baseline methods. Our method establishes a new paradigm for efficient multimodal medical image fusion suitable for real-time clinical deployment.