Latest AI and machine learning research in schizophrenia for healthcare professionals.
Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the curved geometry of correlation space. Existing geometric methods frequently lack closed-form operations or depend on arbitrary region ordering, limiting scalability. We introduce a scalable geometric framework with two components: (i) the Off-log metric...
Deep learning-based structure prediction enables the design of peptide ligands without relying on naturally occurring scaffolds. However, most computationally generated peptides are not advanced beyond initial activity measurements, leaving the path to drug-like optimization and in vivo validation underexplored. Here we establish an end-to-end workflow for de novo peptide agonist discovery and mat...
Motivation: The ToxCast database is a valuable resource for computational toxicology and new approach methodologies (NAMs), but the approximately 100G...
Synthetic data is widely used in healthcare to create datasets that are similar to original data but without the privacy concerns. Generating and eval...
Multimodal large language models (MLLMs) have become a key interface for visual reasoning and grounded question answering, yet they remain vulnerable ...
Vision-language models typically reason over post-ISP RGB images, although RGB rendering can clip, suppress, or quantize sensor evidence before infere...
Randomized neural networks (RdNNs) enable efficient, backpropagation-free training by freezing randomly initialized input-to-hidden weights, which per...
Foundation diffusion models can generate photorealistic natural images, but adapting them to medical imaging remains challenging. In medical adaptatio...
Large vision-language models (VLMs) demonstrate strong performance in medical image understanding, but frequently generate clinically plausible yet in...
Long-form video understanding remains challenging for Video Large Language Models (VideoLLMs), as the dense frame sampling introduces massive visual t...
Large language models are increasingly deployed in clinical decision-support contexts, yet systematic evaluation of their factual reliability in gener...
Despite the breakneck pace of progress in protein design methodology, frontier problems remain challenging, with leading methods struggling to design ...
Magnetic Resonance Imaging (MRI) acquisition remains a time-intensive and patient-straining process, as prolonged scan dura- tions increase the likeli...
Ground-to-space astronomical super-resolution requires recovering space-quality images from ground-based observations that are simultaneously limited ...
Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image...
Vision-Language Models (VLMs) exhibit strong performance in instruction following and open-ended vision-language reasoning, yet they frequently genera...
Vision-language models (VLMs) are increasingly deployed as evaluators in tasks requiring nuanced image understanding, yet their reliability in scoring...
Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding,...
Vision evaluations are typically done through multi-step processes. In most contemporary fields, experts analyze images using structured, evidence-bas...
Text-to-Image generation has seen significant advancements in output realism with the advent of diffusion models. However, diffusion models encounter ...