Continual learning is essential for medical image classification systems to adapt to dynamically evolving clinical environments. The integration of multimodal information can significantly enhance continual learning of image classes. However, while existing approaches do utilize textual modality information, they solely rely on simplistic templates with a class name, thereby neglecting richer semantic information. To address these limitations, we propose a novel framework that harnesses visual concepts generated by large language models (LLMs) as discriminative semantic guidance. Our method dynamically constructs a visual concept pool with a similarity-based filtering mechanism to prevent redundancy. Then, to integrate the concepts into the continual learning process, we employ a cross-modal image-concept attention module, coupled with an attention loss. Through attention, the module can leverage the semantic knowledge from relevant visual concepts and produce class-representative fused features for classification. Experiments on medical and natural image datasets show our method achieves state-of-the-art performance, demonstrating the effectiveness and superiority of our method. We will release the code publicly.

The development of efficient segmentation strategies for medical images has evolved from its initial dependence on Convolutional Neural Networks (CNNs) to the current investigation of hybrid models that combine CNNs with Vision Transformers (ViTs). There is an increasing focus on developing architectures that are both high-performing and computationally efficient, capable of being deployed on remote systems with limited resources. Although transformers can capture global dependencies in the input space, they face challenges from the corresponding high computational and storage expenses involved. The objective of this research is to propose that Vision Extended Long Short-Term Memory (Vision-xLSTM) forms an appropriate backbone for medical image segmentation, offering excellent performance with reduced computational costs. This study investigates the integration of CNNs with Vision-xLSTM by introducing the novel U-VixLSTM. The Vision-xLSTM blocks capture the temporal and global relationships within the patches extracted from the CNN feature maps. The convolutional feature reconstruction path upsamples the output volume from the Vision-xLSTM blocks to produce the segmentation output. The U-VixLSTM exhibits superior performance compared to the state-of-the-art networks in the publicly available Synapse, ISIC and ACDC datasets. The findings suggest that U-VixLSTM is a promising alternative to ViTs for medical image segmentation, delivering effective performance without substantial computational burden. This makes it feasible for deployment in healthcare environments with limited resources for faster diagnosis. Code provided: https://github.com/duttapallabi2907/U-VixLSTM.

Medical segmentation, a prominent task in medical image analysis utilizing artificial intelligence, plays a crucial role in computer-aided diagnosis and depends heavily on the quality of the training data. However, the availability of sufficient data is constrained by strict privacy regulations associated with medical data. To mitigate this issue, research on data augmentation has gained significant attention. Medical segmentation tasks require paired datasets consisting of medical images and annotation images, also known as mask images, which represent lesion areas or radiological information within the medical images. Consequently, it is essential to apply data augmentation to both image types. This study proposes a Controllable Mask Diffusion Model, a novel approach capable of controlling and generating new masks. This model leverages the binary structure of the mask to extract semantic information, namely, the mask's size, location, and count, which is then applied as multi-conditional input to a diffusion model via a regressor. Through the regressor, newly generated masks conform to the input semantic information, thereby enabling input-driven controllable generation. Additionally, a technique that analyzes correlation within semantic information was devised for large-scale data synthesis. The generative capacity of the proposed model was evaluated against real datasets, and the model's ability to control and generate new masks based on previously unseen semantic information was confirmed. Furthermore, the practical applicability of the model was demonstrated by augmenting the data with the generated data, applying it to segmentation tasks, and comparing the performance with and without augmentation. Additionally, experiments were conducted on single-label and multi-label masks, yielding superior results for both types. This demonstrates the potential applicability of this study to various areas within the medical field.

The REgistry of Flow and Perfusion Imaging for Artificial Intelligence with PET (REFINE PET) was established to collect multicenter PET and associated computed tomography (CT) images, together with clinical data and outcomes, into a comprehensive research resource. REFINE PET will enable validation and development of both standard and novel cardiac PET/CT processing methods. REFINE PET is a multicenter, international registry that contains both clinical and imaging data. The PET scans were processed using QPET software (Cedars-Sinai Medical Center, Los Angeles, CA), while the CT scans were processed using deep learning (DL) to detect coronary artery calcium (CAC). Patients were followed up for the occurrence of major adverse cardiovascular events (MACE), which include death, myocardial infarction, unstable angina, and late revascularization (>90 days from PET). The REFINE PET registry currently contains data for 35,588 patients from 14 sites, with additional patient data and sites anticipated. Comprehensive clinical data (including demographics, medical history, and stress test results) were integrated with more than 2200 imaging variables across 42 categories. The registry is poised to address a broad range of clinical questions, supported by correlating invasive angiography (within 6 months of MPI) in 5972 patients and a total of 9252 major adverse cardiovascular events during a median follow-up of 4.2 years. The REFINE PET registry leverages the integration of clinical, multimodality imaging, and novel quantitative and AI tools to advance the role of PET/CT MPI in diagnosis and risk stratification.

Image registration is essential for medical image applications where alignment of voxels across multiple images is needed for qualitative or quantitative analysis. With recent advances in deep neural networks and parallel computing, deep learning-based medical image registration methods become competitive with their flexible modeling and fast inference capabilities. However, compared to traditional optimization-based registration methods, the speed advantage may come at the cost of registration performance at inference time. Besides, deep neural networks ideally demand large training datasets while optimization-based methods are training-free. To improve registration accuracy and data efficiency, we propose a novel image registration method, termed Recurrent Inference Image Registration (RIIR) network. RIIR is formulated as a meta-learning solver for the registration problem in an iterative manner. RIIR addresses the accuracy and data efficiency issues, by learning the update rule of optimization, with implicit regularization combined with explicit gradient input. We extensively evaluated RIIR on brain MRI, lung CT, and quantitative cardiac MRI datasets, in terms of both registration accuracy and training data efficiency. Our experiments showed that RIIR outperformed a range of deep learning-based methods, even with only 5% of the training data, demonstrating high data efficiency. Key findings from our ablation studies highlighted the important added value of the hidden states introduced in the recurrent inference framework for meta-learning. Our proposed RIIR offers a highly data-efficient framework for deep learning-based medical image registration.

This systematic review and meta-analysis evaluated the performance of imaging-based artificial intelligence (AI) models in diagnosing preoperative cervical lymph node metastasis (LNM) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC). We conducted a literature search in PubMed, Embase, and Web of Science until February 25, 2025. Studies were selected that focused on imaging-based AI models for predicting cervical LNM in cN0 PTC. The diagnostic performance metrics were analyzed using a bivariate random-effects model, and study quality was assessed with the QUADAS-2 tool. From 671 articles, 11 studies involving 3366 patients were included. Ultrasound (US)-based AI models showed pooled sensitivity of 0.79 and specificity of 0.82, significantly higher than radiologists (p < 0.001). CT-based AI models demonstrated sensitivity of 0.78 and specificity of 0.89. Imaging-based AI models, particularly US-based AI, show promising diagnostic performance. There is a need for further multicenter prospective studies for validation. PROSPERO: (CRD420251063416).

Artificial intelligence (AI) is increasingly being explored as a complementary tool to traditional fracture risk assessment methods. Conventional approaches, such as bone mineral density measurement and established clinical risk calculators, provide populationlevel stratification but often fail to capture the structural nuances of bone fragility. Recent advances in AI-particularly deep learning techniques applied to imaging-enable opportunistic screening and individualized risk estimation using routinely acquired radiographs and computed tomography (CT) data. These models demonstrate improved discrimination for osteoporotic fracture detection and risk prediction, supporting applications such as time-to-event modeling and short-term prognosis. CT- and radiograph-based models have shown superiority over conventional metrics in diverse cohorts, while innovations like multitask learning and survival plots contribute to enhanced interpretability and patient-centered communication. Nevertheless, challenges related to model generalizability, data bias, and automation bias persist. Successful clinical integration will require rigorous external validation, transparent reporting, and seamless embedding into electronic medical systems. This review summarizes recent advances in AI-driven fracture assessment, critically evaluates their clinical promise, and outlines a roadmap for translation into real-world practice.

Medical image segmentation is crucial for disease diagnosis and treatment planning, yet developing robust segmentation models often requires substantial computational resources and large datasets. Existing research shows that pre-trained and finetuned foundation models can boost segmentation performance. However, questions remain about how particular image preprocessing steps may influence segmentation performance across different medical imaging modalities. In particular, edges-abrupt transitions in pixel intensity-are widely acknowledged as vital cues for object boundaries but have not been systematically examined in the pre-training of foundation models. We address this gap by investigating to which extend pre-training with data processed using computationally efficient edge kernels, such as kirsch, can improve cross-modality segmentation capabilities of a foundation model. Two versions of a foundation model are first trained on either raw or edge-enhanced data across multiple medical imaging modalities, then finetuned on selected raw subsets tailored to specific medical modalities. After systematic investigation using the medical domains Dermoscopy, Fundus, Mammography, Microscopy, OCT, US, and XRay, we discover both increased and reduced segmentation performance across modalities using edge-focused pre-training, indicating the need for a selective application of this approach. To guide such selective applications, we propose a meta-learning strategy. It uses standard deviation and image entropy of the raw image to choose between a model pre-trained on edge-enhanced or on raw data for optimal performance. Our experiments show that integrating this meta-learning layer yields an overall segmentation performance improvement across diverse medical imaging tasks by 16.42% compared to models pre-trained on edge-enhanced data only and 19.30% compared to models pre-trained on raw data only.

This study investigates how deep learning (DL) can enhance ovarian cancer diagnosis and staging using large imaging datasets. Specifically, we compare six conventional convolutional neural network (CNN) architectures-ResNet, DenseNet, GoogLeNet, U-Net, VGG, and AlexNet-with OCDA-Net, an enhanced model designed for [¹⁸F]FDG PET image analysis. The OCDA-Net, an advancement on the ResNet architecture, was thoroughly compared using randomly split datasets of training (80%), validation (10%), and test (10%) images. Trained over 100 epochs, OCDA-Net achieved superior diagnostic classification with an accuracy of 92%, and staging results of 94%, supported by robust precision, recall, and F-measure metrics. Grad-CAM ++ heat-maps confirmed that the network attends to hyper-metabolic lesions, supporting clinical interpretability. Our findings show that OCDA-Net outperforms existing CNN models and has strong potential to transform ovarian cancer diagnosis and staging. The study suggests that implementing these DL models in clinical practice could ultimately improve patient prognoses. Future research should expand datasets, enhance model interpretability, and validate these models in clinical settings.

Proton therapy is commonly used for treating hepatocellular carcinoma (HCC); however, its feasibility can be challenging to assess in large tumors or those adjacent to critical organs at risk (OARs), which are typically assessed only after planning computed tomography (CT) acquisition. This study aimed to predict proton dose distributions using diagnostic CT (dCT) and diagnostic MRI (dMRI) with a convolutional neural network (CNN), enabling early treatment feasibility assessments. Dose distributions and dose-volume histograms (DVHs) were calculated for 118 patients with HCC using intensity-modulated proton therapy (IMPT) and passive proton therapy. A CPU-based CNN model was used to predict DVHs and 3D dose distributions from diagnostic images. Prediction accuracy was evaluated using mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and gamma passing rate with a 3 mm/3% criterion. The predicted DVHs and dose distributions showed high agreement with actual values. MAE remained below 3.0%, with passive techniques achieving 1.2-1.8%. MSE was below 0.004 in all cases. PSNR ranged from 24 to 28 dB, and SSIM exceeded 0.94 in most conditions. Gamma passing rates averaged 82-83% for IMPT and 92-93% for passive techniques. The model achieved comparable accuracy when using dMRI and dCT. This study demonstrates that early dose distribution prediction from diagnostic imaging is feasible and accurate using a lightweight CNN model. Despite anatomical variability between diagnostic and planning images, this approach provides timely insights into treatment feasibility, potentially supporting insurance pre-authorization, reducing unnecessary imaging, and optimizing clinical workflows for HCC proton therapy.