Latest AI and machine learning research in schizophrenia for healthcare professionals.
Recent advances in fMRI-based image reconstruction have achieved remarkable photo-realistic fidelity. Yet, a persistent limitation remains: while reconstructed images often appear naturalistic and holistically similar to the target stimuli, they frequently suffer from severe semantic misalignment -- salient objects are often replaced or hallucinated despite high visual quality. In this work, we ad...
Real-world text image super-resolution aims to restore overall visual quality and text legibility in images suffering from diverse degradations and text distortions. However, the scarcity of text image data in existing datasets results in poor performance on text regions. In addition, datasets consisting of isolated text samples limit the quality of background reconstruction. To address these limi...
High-precision facial landmark detection (FLD) relies on high-resolution deep feature representations. However, low-resolution face images or the comp...
We introduce ELITE, an Efficient Gaussian head avatar synthesis from a monocular video via Learned Initialization and TEst-time generative adaptation....
AI agents are increasingly used in long, multi-turn workflows in both research and enterprise settings. As interactions grow, agent behavior often deg...
Adverse events such as compulsory measures, absconding, illicit substance use, self-harm, aggressive behavior, and prolonged hospitalization pose sign...
BACKGROUND: The combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic feat...
We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a nove...
We propose a unified food-domain QA framework that combines a large-scale multimodal knowledge graph (MMKG) with generative AI. Our MMKG links 13,00...
Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from hallucination i...
Hallucinations in large vision-language models (LVLMs) pose significant challenges for real-world applications, as LVLMs may generate responses that...
Despite remarkable progress in image quality and prompt fidelity, text-to-image (T2I) diffusion models continue to exhibit persistent "hallucination...
Despite remarkable progress in image quality and prompt fidelity, text-to-image (T2I) diffusion models continue to exhibit persistent "hallucination...
BACKGROUND: The ultimate goal of successful schizophrenia treatment is not just to alleviate psychotic symptoms, but also to reduce distress and achie...
Diffusion models, originally developed for image generation, have emerged as a promising alternative to autoregressive large language models (LLMs)....
This work presents a comprehensive theory of consciousness grounded in mathematical formalism and supported by clinical data analysis. The framework...
Recent Large Vision-Language Models (LVLMs) have introduced a new paradigm for understanding and reasoning about image input through textual respons...
BACKGROUND: The cross-modal conflict deficit is a key feature of schizophrenia. However, it remains largely unknown whether cross-modal conflict in sc...
BACKGROUND: People with psychosis have a higher suicide risk than the general population. Natural language processing (NLP) has been used to understan...
Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relat...