Convolutional denoising autoencoders (DAEs) are powerful tools for image restoration. However, they inherit a key limitation of convolutional neural networks (CNNs): they tend to recover low-frequency features, such as smooth regions, more effectively than high-frequency details. This leads to the loss of fine details, which is particularly problematic in dental radiographs where preserving subtle anatomical structures is crucial. While self-attention mechanisms can help mitigate this issue by emphasizing important features, conventional attention methods often prioritize features corresponding to cleaner regions and may overlook those obscured by noise. To address this limitation, we propose a noise-aware self-attention method, which allows the model to effectively focus on and recover key features even within noisy regions. Building on this approach, we introduce the noise-aware attention-enhanced denoising autoencoder (NAADA) network for enhancing noisy panoramic dental radiographs. Compared with the recent state of the art (and much heavier) methods like Uformer, MResDNN etc., our method improves the reconstruction of fine details, ensuring better image quality and diagnostic accuracy.

Computed Tomography (CT) plays a crucial role in clinical diagnosis, but the growing demand for CT examinations has raised concerns about diagnostic errors. While Multimodal Large Language Models (MLLMs) demonstrate promising comprehension of medical knowledge, their tendency to produce inaccurate information highlights the need for rigorous validation. However, existing medical visual question answering (VQA) benchmarks primarily focus on simple visual recognition tasks, lacking clinical relevance and failing to assess expert-level knowledge. We introduce MedErr-CT, a novel benchmark for evaluating medical MLLMs' ability to identify and correct errors in CT reports through a VQA framework. The benchmark includes six error categories - four vision-centric errors (Omission, Insertion, Direction, Size) and two lexical error types (Unit, Typo) - and is organized into three task levels: classification, detection, and correction. Using this benchmark, we quantitatively assess the performance of state-of-the-art 3D medical MLLMs, revealing substantial variation in their capabilities across different error types. Our benchmark contributes to the development of more reliable and clinically applicable MLLMs, ultimately helping reduce diagnostic errors and improve accuracy in clinical practice. The code and datasets are available at https://github.com/babbu3682/MedErr-CT.

Understanding medical ultrasound imaging remains a long-standing challenge due to significant visual variability caused by differences in imaging and acquisition parameters. Recent advancements in large language models (LLMs) have been used to automatically generate terminology-rich summaries orientated to clinicians with sufficient physiological knowledge. Nevertheless, the increasing demand for improved ultrasound interpretability and basic scanning guidance among non-expert users, e.g., in point-of-care settings, has not yet been explored. In this study, we first introduce the scene graph (SG) for ultrasound images to explain image content to ordinary and provide guidance for ultrasound scanning. The ultrasound SG is first computed using a transformer-based one-stage method, eliminating the need for explicit object detection. To generate a graspable image explanation for ordinary, the user query is then used to further refine the abstract SG representation through LLMs. Additionally, the predicted SG is explored for its potential in guiding ultrasound scanning toward missing anatomies within the current imaging view, assisting ordinary users in achieving more standardized and complete anatomical exploration. The effectiveness of this SG-based image explanation and scanning guidance has been validated on images from the left and right neck regions, including the carotid and thyroid, across five volunteers. The results demonstrate the potential of the method to maximally democratize ultrasound by enhancing its interpretability and usability for ordinaries.

BackgroundComputer-aided detection (CAD) software analyzes chest X-rays for features suggestive of tuberculosis (TB) and provides a numeric abnormality score. However, estimates of CAD accuracy for TB screening are hindered by the lack of confirmatory data among people with lower CAD scores, including those without symptoms. Additionally, the appropriate CAD score thresholds for obtaining further testing may vary according to population and client characteristics. MethodsWe screened for TB in Ugandan individuals aged [&ge;]15 years using portable chest X-rays with CAD (qXR v3). Participants were offered screening regardless of their symptoms. Those with X-ray scores above a threshold of 0.1 (range, 0 - 1) were asked to provide sputum for Xpert Ultra testing. We estimated the diagnostic accuracy of CAD for detecting Xpert-positive TB when using the same threshold for all individuals (under different assumptions about TB prevalence among people with X-ray scores <0.1), and compared this estimate to age- and/or sex-stratified approaches. FindingsOf 52,835 participants screened for TB using CAD, 8,949 (16.9%) had X-ray scores [&ge;]0.1. Of 7,219 participants with valid Xpert Ultra results, 382 (5.3%) were Xpert-positive, including 81 with trace results. Assuming 0.1% of participants with X-ray scores <0.1 would have been Xpert-positive if tested, qXR had an estimated AUC of 0.920 (95% confidence interval 0.898-0.941) for Xpert-positive TB. Stratifying CAD thresholds according to age and sex improved accuracy; for example, at 96.1% specificity, estimated sensitivity was 75.0% for a universal threshold (of [&ge;]0.65) versus 76.9% for thresholds stratified by age and sex (p=0.046). InterpretationThe accuracy of CAD for TB screening among all screening participants, including those without symptoms or abnormal chest X-rays, is higher than previously estimated. Stratifying CAD thresholds based on client characteristics such as age and sex could further improve accuracy, enabling a more effective and personalized approach to TB screening. FundingNational Institutes of Health Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe World Health Organization (WHO) has endorsed computer-aided detection (CAD) as a screening tool for tuberculosis (TB), but the appropriate CAD score that triggers further diagnostic evaluation for tuberculosis varies by population. The WHO recommends determining the appropriate CAD threshold for specific settings and population and considering unique thresholds for specific populations, including older age groups, among whom CAD may perform poorly. We performed a PubMed literature search for articles published until September 9, 2024, using the search terms "tuberculosis" AND ("computer-aided detection" OR "computer aided detection" OR "CAD" OR "computer-aided reading" OR "computer aided reading" OR "artificial intelligence"), which resulted in 704 articles. Among them, we identified studies that evaluated the performance of CAD for tuberculosis screening and additionally reviewed relevant references. Most prior studies reported area under the curves (AUC) ranging from 0.76 to 0.88 but limited their evaluations to individuals with symptoms or abnormal chest X-rays. Some prior studies identified subgroups (including older individuals and people with prior TB) among whom CAD had lower-than-average AUCs, and authors discussed how the prevalence of such characteristics could affect the optimal value of a population-wide CAD threshold; however, none estimated the accuracy that could be gained with adjusting CAD thresholds between individuals based on personal characteristics. Added value of this studyIn this study, all consenting individuals in a high-prevalence setting were offered chest X-ray screening, regardless of symptoms, if they were [&ge;]15 years old, not pregnant, and not on TB treatment. A very low CAD score cutoff (qXR v3 score of 0.1 on a 0-1 scale) was used to select individuals for confirmatory sputum molecular testing, enabling the detection of radiographically mild forms of TB and facilitating comparisons of diagnostic accuracy at different CAD thresholds. With this more expansive, symptom-neutral evaluation of CAD, we estimated an AUC of 0.920, and we found that the qXR v3 threshold needed to decrease to under 0.1 to meet the WHO target product profile goal of [&ge;]90% sensitivity and [&ge;]70% specificity. Compared to using the same thresholds for all participants, adjusting CAD thresholds by age and sex strata resulted in a 1 to 2% increase in sensitivity without affecting specificity. Implications of all the available evidenceTo obtain high sensitivity with CAD screening in high-prevalence settings, low score thresholds may be needed. However, countries with a high burden of TB often do not have sufficient resources to test all individuals above a low threshold. In such settings, adjusting CAD thresholds based on individual characteristics associated with TB prevalence (e.g., male sex) and those associated with false-positive X-ray results (e.g., old age) can potentially improve the efficiency of TB screening programs.

Medical image segmentation is challenging due to overlapping anatomies with ambiguous boundaries and a severe imbalance between the foreground and background classes, which particularly affects the delineation of small lesions. Existing methods, including encoder-decoder networks and prompt-driven variants of the Segment Anything Model (SAM), rely heavily on local cues or user prompts and lack integrated semantic priors, thus failing to generalize well to low-contrast or overlapping targets. To address these issues, we propose MedSeg-R, a lightweight, dual-stage framework inspired by inspired by clinical reasoning. Its cognitive stage interprets medical report into structured semantic priors (location, texture, shape), which are fused via transformer block. In the perceptual stage, these priors modulate the SAM backbone: spatial attention highlights likely lesion regions, dynamic convolution adapts feature filters to expected textures, and deformable sampling refines spatial support. By embedding this fine-grained guidance early, MedSeg-R disentangles inter-class confusion and amplifies minority-class cues, greatly improving sensitivity to small lesions. In challenging benchmarks, MedSeg-R produces large Dice improvements in overlapping and ambiguous structures, demonstrating plug-and-play compatibility with SAM-based systems.

Artificial Intelligence (AI) holds significant promise for improving prognosis prediction in medical imaging, yet its effective application remains challenging. In this work, we introduce a structured benchmark explicitly designed to evaluate and compare the transferability of Convolutional Neural Networks and Foundation Models in predicting clinical outcomes in COVID-19 patients, leveraging diverse publicly available Chest X-ray datasets. Our experimental methodology extensively explores a wide set of fine-tuning strategies, encompassing traditional approaches such as Full Fine-Tuning and Linear Probing, as well as advanced Parameter-Efficient Fine-Tuning methods including Low-Rank Adaptation, BitFit, VeRA, and IA3. The evaluations were conducted across multiple learning paradigms, including both extensive full-data scenarios and more clinically realistic Few-Shot Learning settings, which are critical for modeling rare disease outcomes and rapidly emerging health threats. By implementing a large-scale comparative analysis involving a diverse selection of pretrained models, including general-purpose architectures pretrained on large-scale datasets such as CLIP and DINOv2, to biomedical-specific models like MedCLIP, BioMedCLIP, and PubMedCLIP, we rigorously assess each model's capacity to effectively adapt and generalize to prognosis tasks, particularly under conditions of severe data scarcity and pronounced class imbalance. The benchmark was designed to capture critical conditions common in prognosis tasks, including variations in dataset size and class distribution, providing detailed insights into the strengths and limitations of each fine-tuning strategy. This extensive and structured evaluation aims to inform the practical deployment and adoption of robust, efficient, and generalizable AI-driven solutions in real-world clinical prognosis prediction workflows.

As the deep learning revolution marches on, masked modeling has emerged as a distinctive approach that involves predicting parts of the original data that are proportionally masked during training, and has demonstrated exceptional performance in multiple fields. Magnetic Resonance Imaging (MRI) reconstruction is a critical task in medical imaging that seeks to recover high-quality images from under-sampled k-space data. However, previous MRI reconstruction strategies usually optimized the entire image domain or k-space, without considering the importance of different frequency regions in the k-space This work introduces a diffusion model based on adaptive masks (AMDM), which utilizes the adaptive adjustment of frequency distribution based on k-space data to develop a hybrid masks mechanism that adapts to different k-space inputs. This enables the effective separation of high-frequency and low-frequency components, producing diverse frequency-specific representations. Additionally, the k-space frequency distribution informs the generation of adaptive masks, which, in turn, guide a closed-loop diffusion process. Experimental results verified the ability of this method to learn specific frequency information and thereby improved the quality of MRI reconstruction, providing a flexible framework for optimizing k-space data using masks in the future.

Foundation models for volumetric medical image segmentation have emerged as powerful tools in clinical workflows, enabling radiologists to delineate regions of interest through intuitive clicks. While these models demonstrate promising capabilities in segmenting previously unseen anatomical structures, their performance is strongly influenced by prompt quality. In clinical settings, radiologists often provide suboptimal prompts, which affects segmentation reliability and accuracy. To address this limitation, we present SafeClick, an error-tolerant interactive segmentation approach for medical volumes based on hierarchical expert consensus. SafeClick operates as a plug-and-play module compatible with foundation models including SAM 2 and MedSAM 2. The framework consists of two key components: a collaborative expert layer (CEL) that generates diverse feature representations through specialized transformer modules, and a consensus reasoning layer (CRL) that performs cross-referencing and adaptive integration of these features. This architecture transforms the segmentation process from a prompt-dependent operation to a robust framework capable of producing accurate results despite imperfect user inputs. Extensive experiments across 15 public datasets demonstrate that our plug-and-play approach consistently improves the performance of base foundation models, with particularly significant gains when working with imperfect prompts. The source code is available at https://github.com/yifangao112/SafeClick.

Segmenting biomarkers in medical images is crucial for various biotech applications. Despite advances, Transformer and CNN based methods often struggle with variations in staining and morphology, limiting feature extraction. In medical image segmentation, where datasets often have limited sample availability, recent state-of-the-art (SOTA) methods achieve higher accuracy by leveraging pre-trained encoders, whereas end-to-end methods tend to underperform. This is due to challenges in effectively transferring rich multiscale features from encoders to decoders, as well as limitations in decoder efficiency. To address these issues, we propose an architecture that captures multi-scale local and global contextual information and a novel decoder design, which effectively integrates features from the encoder, emphasizes important channels and regions, and reconstructs spatial dimensions to enhance segmentation accuracy. Our method, compatible with various encoders, outperforms SOTA methods, as demonstrated by experiments on four datasets and ablation studies. Specifically, our method achieves absolute performance gains of 2.76% on MoNuSeg, 3.12% on DSB, 2.87% on Electron Microscopy, and 4.03% on TNBC datasets compared to existing SOTA methods. Code: https://github.com/saadwazir/MCADS-Decoder

Existing foundation models for neuroimaging are often prohibitively large and data-intensive. We introduce BrainSymphony, a lightweight, parameter-efficient foundation model that achieves state-of-the-art performance while being pre-trained on significantly smaller public datasets. BrainSymphony's strong multimodal architecture processes functional MRI data through parallel spatial and temporal transformer streams, which are then efficiently distilled into a unified representation by a Perceiver module. Concurrently, it models structural connectivity from diffusion MRI using a novel signed graph transformer to encode the brain's anatomical structure. These powerful, modality-specific representations are then integrated via an adaptive fusion gate. Despite its compact design, our model consistently outperforms larger models on a diverse range of downstream benchmarks, including classification, prediction, and unsupervised network identification tasks. Furthermore, our model revealed novel insights into brain dynamics using attention maps on a unique external psilocybin neuroimaging dataset (pre- and post-administration). BrainSymphony establishes that architecturally-aware, multimodal models can surpass their larger counterparts, paving the way for more accessible and powerful research in computational neuroscience.