Latest AI and machine learning research in adhd/add for healthcare professionals.
Brain imaging classification is commonly approached from two perspectives: modeling the full image volume to capture global anatomical context, or constructing ROI-based graphs to encode localized and topological interactions. Although both representations have demonstrated independent efficacy, their relative contributions and potential complementarity remain insufficiently understood. Existing f...
Uncovering the genetic architecture of quantitative traits is challenging because polygenic control yields small individual gene effects and because gene-gene and genotype-by-environment interactions add further complexity. To understand the genetic basis of polygenic traits and their plasticity across environments, we integrated genome-wide SNPs and RNA-seq transcript data with interpretable stat...
Vision transformers have demonstrated remarkable success in classification by leveraging global self-attention to capture long-range dependencies. How...
Estimating the 6D pose of objects from a single RGB image is a critical task for robotics and extended reality applications. However, state-of-the-art...
Inhibition is a core cognitive control function whose competence is distributed across the population, with more extreme impairments in psychiatric co...
The image purification strategy constructs an intermediate distribution with aligned anatomical structures, which effectively corrects the spatial mis...
Single-image 3D generation with part-level structure remains challenging: learned priors struggle to cover the long tail of part geometries and mainta...
Generating diagnostic text from histopathology whole slide images (WSIs) is challenging due to the gigapixel scale of the input and the requirement fo...
When learning to find the most beneficial course of action, the prefrontal cortex guides decisions by comparing estimates of the relative value of the...
Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different sympt...
Large language models (LLMs) continue to struggle with knowledge-intensive questions that require up-to-date information and multi-hop reasoning. Augm...
Self-supervised learning (SSL) methods based on Siamese networks learn visual representations by aligning different views of the same image. The multi...
We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEPAs). JEPAs...
Multiple-instance Learning (MIL) is commonly used to undertake computational pathology (CPath) tasks, and the use of multi-scale patches allows divers...
Dopamine (DA) has been implicated in exploration-exploitation behaviour, i.e., exploring novel, potentiallybetter options vs. exploiting known, previo...
Data science agents promise to accelerate discovery and insight-generation by turning data into executable analyses and findings. Yet existing data sc...
Self-supervised learning is increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on pair...
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the u...
Accurate segmentation of polyps from colonoscopy images is crucial for the early diagnosis and treatment of colorectal cancer. Most existing deep le...
Accurately converting pixel measurements into absolute real-world dimensions remains a fundamental challenge in computer vision and limits progress ...