Automated ultrasound (US) image analysis remains a longstanding challenge due to anatomical complexity and the scarcity of annotated data. Although large-scale pretraining has improved data efficiency in many visual domains, its impact in US is limited by a pronounced domain shift from other imaging modalities and high variability across clinical applications, such as chest, ovarian, and endoscopic imaging. To address this, we propose UltraSam, a SAM-style model trained on a heterogeneous collection of publicly available segmentation datasets, originally developed in isolation. UltraSam is trained under the prompt-conditioned segmentation paradigm, which eliminates the need for unified labels and enables generalization to a broad range of downstream tasks. We compile US-43d, a large-scale collection of 43 open-access US datasets comprising over 282,000 images with segmentation masks covering 58 anatomical structures. We explore adaptation and fine-tuning strategies for SAM and systematically evaluate transferability across downstream tasks, comparing against state-of-the-art pretraining methods. We further propose prompted classification, a new use case where object-specific prompts and image features are jointly decoded to improve classification performance. In experiments on three diverse public US datasets, UltraSam outperforms existing SAM variants on prompt-based segmentation and surpasses self-supervised US foundation models on downstream (prompted) classification and instance segmentation tasks. UltraSam demonstrates that SAM-style training on diverse, sparsely annotated US data enables effective generalization across tasks. By unlocking the value of fragmented public datasets, our approach lays the foundation for scalable, real-world US representation learning. We release our code and pretrained models at https://github.com/CAMMA-public/UltraSam and invite the community to further this effort by continuing to contribute high-quality datasets.

Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of cancer-related mortality worldwide. Although early detection can increase the 5-year survival rate of early gastric cancer (EGC) to over 90%, more than 80% of cases are diagnosed at advanced stages due to subtle clinical symptoms and diagnostic challenges. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown great promise in addressing these limitations. This systematic review aims to evaluate the performance, applications, and limitations of ML and DL models in GC management, with a focus on their use in detection, diagnosis, treatment planning, and prognosis prediction across diverse clinical imaging and data modalities. Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus for studies published between 2004 and May 2025. Eligible studies applied ML or DL algorithms for diagnostic or prognostic tasks in GC using data from endoscopy, computed tomography (CT), pathology, or multi-modal sources. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. A total of 59 studies met the inclusion criteria. DL models, particularly convolutional neural networks (CNNs), demonstrated strong performance in EGC detection, with reported sensitivities up to 95.3% and Area Under the Curve (AUCs) as high as 0.981, often exceeding expert endoscopists. CT-based radiomics and DL models achieved AUCs ranging from 0.825 to 0.972 for tumor staging and metastasis prediction. Pathology-based models reported accuracies up to 100% for EGC detection and AUCs up to 0.92 for predicting treatment response. Cross-modality approaches combining radiomics and pathomics achieved AUCs up to 0.951. Key challenges included algorithmic bias, limited dataset diversity, interpretability issues, and barriers to clinical integration. ML and DL models have demonstrated substantial potential to improve early detection, diagnostic accuracy, and individualized treatment in GC. To advance clinical adoption, future research should prioritize the development of large, diverse datasets, implement explainable AI frameworks, and conduct prospective clinical trials. These efforts will be essential for integrating AI into precision oncology and addressing the increasing global burden of gastric cancer.

We aimed to systematically review the studies that utilized deep learning (DL)-based networks to predict hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) using computed tomography (CT) images. We carried out a comprehensive literature search across four major databases to identify relevant studies. To evaluate the quality of the included studies, we used both the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and the METhodological RadiomICs Score (METRICS) checklists. We then calculated pooled diagnostic estimates and assessed heterogeneity using the I² statistic. To assess the sources of heterogeneity, effects of individual studies, and publication bias, we performed subgroup analysis, sensitivity analysis, and Deek's asymmetry test. Twenty-two studies were included in the qualitative synthesis, of which 11 and 6 were utilized for exclusive DL and combined DL meta-analyses, respectively. We found pooled sensitivity of 0.81 and 0.84, specificity of 0.79 and 0.91, positive diagnostic likelihood ratio (DLR) of 3.96 and 9.40, negative DLR of 0.23 and 0.18, diagnostic odds ratio of 16.97 and 53.51, and area under the curve of 0.87 and 0.89 for exclusive DL-based and combined DL-based models, respectively. Subgroup analysis revealed significant inter-group differences according to the segmentation technique and study quality. DL-based networks showed strong potential in accurately identifying HE in ICH patients. These models may guide earlier targeted interventions such as intensive blood pressure control or administration of hemostatic drugs, potentially leading to improved patient outcomes.

Radiation-induced oropharyngeal mucositis (ROM) is a common and severe side effect of radiotherapy in nasopharyngeal cancer patients, leading to significant clinical complications such as malnutrition, infections, and treatment interruptions. Accurate delineation of the oropharyngeal mucosa (OPM) as an organ-at-risk (OAR) is crucial to minimizing radiation exposure and preventing ROM. This study aims to develop and validate an advanced automatic segmentation model, attention-augmented Swin U-Net transformer (AA-Swin UNETR), for accurate delineation of OPM to improve radiotherapy planning and reduce the incidence of ROM. We proposed a hybrid CNN-transformer model, AA-Swin UNETR, based on the Swin UNETR framework, which integrates hierarchical feature extraction with full-scale attention mechanisms. The model includes a Swin Transformer-based encoder and a CNN-based decoder with residual blocks, connected via a full-scale feature connection scheme. The full-scale attention mechanism enables the model to capture long-range dependencies and multi-level features effectively, enhancing the segmentation accuracy. The model was trained on a dataset of 202 CT scans from Nanfang Hospital, using expert manual delineations as the gold standard. We evaluated the performance of AA-Swin UNETR against state-of-the-art (SOTA) segmentation models, including Swin UNETR, nnUNet, and 3D UX-Net, using geometric and dosimetric evaluation parameters. The geometric metrics include Dice similarity coefficient (DSC), surface DSC (sDSC), volume similarity (VS), Hausdorff distance (HD), precision, and recall. The dosimetric metrics include changes of D_0.1 cc and D_mean between results derived from manually delineated OPM and auto-segmentation models. The AA-Swin UNETR model achieved the highest mean DSC of 87.72 ± 1.98%, significantly outperforming Swin UNETR (83.53 ± 2.59%), nnUNet (85.48%± 2.68), and 3D UX-Net (80.04 ± 3.76%). The model also showed superior mean sDSC (98.44 ± 1.08%), mean VS (97.86 ± 1.43%), mean precision (87.60 ± 3.06%) and mean recall (89.22 ± 2.70%), with a competitive mean HD of 9.03 ± 2.79 mm. For dosimetric evaluation, the proposed model generates smallest mean [Formula: see text] (0.46 ± 4.92 cGy) and mean [Formula: see text] (6.26 ± 24.90 cGY) relative to manual delineation compared with other auto-segmentation results (mean [Formula: see text] of Swin UNETR = -0.56 ± 7.28 cGy, nnUNet = 0.99 ± 4.73 cGy, 3D UX-Net = -0.65 ± 8.05 cGy; mean [Formula: see text] of Swin UNETR = 7.46 ± 43.37, nnUNet = 21.76 ± 37.86 and 3D UX-Net = 44.61 ± 62.33). In this paper, we proposed a transformer and CNN hybrid deep-learning based model AA-Swin UNETR for automatic segmentation of OPM as an OAR structure in radiotherapy planning. Evaluations with geometric and dosimetric parameters demonstrated AA-Swin UNETR can generate delineations close to a manual reference, both in terms of geometry and dose-volume metrics. The proposed model out-performed existing SOTA models in both evaluation metrics and demonstrated is capability of accurately segmenting complex anatomical structures of the OPM, providing a reliable tool for enhancing radiotherapy planning.

Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthesis that aligns with both predicted growth and patient anatomy. We train our model on the BraTS adult and pediatric glioma datasets and evaluate on 60 axial slices of in-house longitudinal pediatric diffuse midline glioma (DMG) cases. Our framework generates realistic follow-up scans based on spatial similarity metrics. It also introduces tumor growth probability maps, which capture both clinically relevant extent and directionality of tumor growth as shown by 95th percentile Hausdorff Distance. The method enables biologically informed image generation in data-limited scenarios, offering generative-space-time predictions that account for mechanistic priors.

Three-dimensional ultrasound (US) offers many clinical advantages over conventional 2D imaging, yet its widespread adoption is limited by the cost and complexity of traditional 3D systems. Sensorless 3D US, which uses deep learning to estimate a 3D probe trajectory from a sequence of 2D US images, is a promising alternative. Local features, such as speckle patterns, can help predict frame-to-frame motion, while global features, such as coarse shapes and anatomical structures, can situate the scan relative to anatomy and help predict its general shape. In prior approaches, global features are either ignored or tightly coupled with local feature extraction, restricting the ability to robustly model these two complementary aspects. We propose DualTrack, a novel dual-encoder architecture that leverages decoupled local and global encoders specialized for their respective scales of feature extraction. The local encoder uses dense spatiotemporal convolutions to capture fine-grained features, while the global encoder utilizes an image backbone (e.g., a 2D CNN or foundation model) and temporal attention layers to embed high-level anatomical features and long-range dependencies. A lightweight fusion module then combines these features to estimate the trajectory. Experimental results on a large public benchmark show that DualTrack achieves state-of-the-art accuracy and globally consistent 3D reconstructions, outperforming previous methods and yielding an average reconstruction error below 5 mm.

The diffusion MRI Neurite Exchange Imaging model offers a promising framework for probing gray matter microstructure by estimating parameters such as compartment sizes, diffusivities, and inter-compartmental water exchange time. However, existing protocols require long scan times. This study proposes a reduced acquisition scheme for the Connectome 2.0 scanner that preserves model accuracy while substantially shortening scan duration. We developed a data-driven framework using explainable artificial intelligence with a guided recursive feature elimination strategy to identify an optimal 8-feature subset from a 15-feature protocol. The performance of this optimized protocol was validated in vivo and benchmarked against the full acquisition and alternative reduction strategies. Parameter accuracy, preservation of anatomical contrast, and test-retest reproducibility were assessed. The reduced protocol yielded parameter estimates and cortical maps comparable to the full protocol, with low estimation errors in synthetic data and minimal impact on test-retest variability. Compared to theory-driven and heuristic reduction schemes, the optimized protocol demonstrated superior robustness, reducing the deviation in water exchange time estimates by over two-fold. In conclusion, this hybrid optimization framework enables viable imaging of neurite exchange in 14 minutes without loss of parameter fidelity. This approach supports the broader application of exchange-sensitive diffusion magnetic resonance imaging in neuroscience and clinical research, and offers a generalizable method for designing efficient acquisition protocols in biophysical parameter mapping.

Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's disease (AD) from MRI scans. However, DL algorithms can suffer from shortcut learning, in which spurious features, not directly related to the output label, are used for prediction. When these features are related to protected attributes, they can lead to performance bias against underrepresented protected groups, such as those defined by race and sex. In this work, we explore the potential for shortcut learning and demographic bias in DL based AD diagnosis from MRI. We first investigate if DL algorithms can identify race or sex from 3D brain MRI scans to establish the presence or otherwise of race and sex based distributional shifts. Next, we investigate whether training set imbalance by race or sex can cause a drop in model performance, indicating shortcut learning and bias. Finally, we conduct a quantitative and qualitative analysis of feature attributions in different brain regions for both the protected attribute and AD classification tasks. Through these experiments, and using multiple datasets and DL models (ResNet and SwinTransformer), we demonstrate the existence of both race and sex based shortcut learning and bias in DL based AD classification. Our work lays the foundation for fairer DL diagnostic tools in brain MRI. The code is provided at https://github.com/acharaakshit/ShortMR

Objective. Proton beams enable localized dose delivery. Accurate range estimation is essential, but planning still relies on X-ray CT, which introduces uncertainty in stopping power and range. Proton CT measures water equivalent thickness directly but suffers resolution loss from multiple Coulomb scattering. We develop a data driven method that reconstructs water equivalent path length (WEPL) maps from energy resolved proton radiographs, bypassing intermediate reconstructions. Approach. We present a machine learning pipeline for WEPL from high dimensional radiographs. Data were generated with the TOPAS Monte Carlo toolkit, modeling a clinical nozzle and a patient CT. Proton energies spanned 70-230 MeV across 72 projection angles. Principal component analysis reduced input dimensionality while preserving signal. A conditional GAN with gradient penalty was trained for WEPL prediction using a composite loss (adversarial, MSE, SSIM, perceptual) to balance sharpness, accuracy, and stability. Main results. The model reached a mean relative WEPL deviation of 2.5 percent, an SSIM of 0.97, and a proton radiography gamma index passing rate of 97.1 percent (2 percent delta WEPL, 3 mm distance-to-agreement) on a simulated head phantom. Results indicate high spatial fidelity and strong structural agreement. Significance. WEPL can be mapped directly from proton radiographs with deep learning while avoiding intermediate steps. The method mitigates limits of analytic techniques and may improve treatment planning. Future work will tune the number of PCA components, include detector response, explore low dose settings, and extend multi angle data toward full proton CT reconstruction; it is compatible with clinical workflows.

Gliomas are the most prevalent type of primary brain tumors, and their accurate segmentation from MRI is critical for diagnosis, treatment planning, and longitudinal monitoring. However, the scarcity of high-quality annotated imaging data in Sub-Saharan Africa (SSA) poses a significant challenge for deploying advanced segmentation models in clinical workflows. This study introduces a robust and computationally efficient deep learning framework tailored for resource-constrained settings. We leveraged a 3D Attention UNet architecture augmented with residual blocks and enhanced through transfer learning from pre-trained weights on the BraTS 2021 dataset. Our model was evaluated on 95 MRI cases from the BraTS-Africa dataset, a benchmark for glioma segmentation in SSA MRI data. Despite the limited data quality and quantity, our approach achieved Dice scores of 0.76 for the Enhancing Tumor (ET), 0.80 for Necrotic and Non-Enhancing Tumor Core (NETC), and 0.85 for Surrounding Non-Functional Hemisphere (SNFH). These results demonstrate the generalizability of the proposed model and its potential to support clinical decision making in low-resource settings. The compact architecture, approximately 90 MB, and sub-minute per-volume inference time on consumer-grade hardware further underscore its practicality for deployment in SSA health systems. This work contributes toward closing the gap in equitable AI for global health by empowering underserved regions with high-performing and accessible medical imaging solutions.