Latest AI and machine learning research in adhd/add for healthcare professionals.
Recent work from Tran et al. (Science, 2026) introduced MULTI-evolve, a framework for protein engineering that combines single-mutant nomination via a protein language model (PLM) or a deep mutational scan (DMS), experimental single- and double-mutant characterization, and neural networks to engineer hyperactive multimutant proteins. The authors attribute the framework's performance to "epistasis-...
Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated. We propose RE-CONFIRM, a framework for evaluating the robustness of potenti...
Pancreatic tumor segmentation in contrast-enhanced computed tomography (CT) is clinically important yet technically challenging: lesions are often sma...
The evaluation of visual editing models remains fragmented across methods and modalities. Existing benchmarks are often tailored to specific paradigms...
ImportanceGuideline-concordant care for young children with attention-deficit/hyperactivity disorder (ADHD) includes recommending parent training in b...
Real-world image super-resolution is particularly challenging for diffusion models because real degradations are complex, heterogeneous, and rarely mo...
As generative models enable rapid creation of high-fidelity images, societal concerns about misinformation and authenticity have intensified. A promis...
With the great success of diffusion models in image generation, diffusion-based image compression is attracting increasing interests. However, due to ...
Background: Previous recommendations on screening for prostate cancer relied on ongoing trials of screening with prostate-specific antigen (PSA), whic...
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose neuroimaging-based diagnosis remains challenging due ...
Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains cha...
Latent diffusion models (LDMs) have recently achieved strong performance in 3D medical image synthesis. However, modalities like cine cardiac MRI (CMR...
Vision-language models (VLMs) like CLIP are trained with the objective of aligning text and image pairs. To improve CLIP-based few-shot image classifi...
This paper introduces Growing Networks with Autonomous Pruning (GNAP) for image classification. Unlike traditional convolutional neural networks, GNAP...
Counting serves as a simple but powerful test of a Large Vision-Language Model's (LVLM's) reasoning; it forces the model to identify each individual o...
With the growing adoption of vision-language-action models and world models in autonomous driving systems, scalable image tokenization becomes crucial...
We present a motion-adaptive temporal attention mechanism for parameter-efficient video generation built upon frozen Stable Diffusion models. Rather t...
We present a training-free framework for continuous and controllable image editing at test time for text-conditioned generative models. In contrast to...
The progressive automation of transport promises to enhance safety and sustainability through shared mobility. Like other vehicles and road users, and...
The remarkable realism of images generated by diffusion models poses critical detection challenges. Current methods utilize reconstruction error as a ...