Psychiatry

Schizophrenia

Latest AI and machine learning research in schizophrenia for healthcare professionals.

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Beyond Dominant Patches: Spatial Credit Redistribution For Grounded Vision-Language Models

Vision-language models (VLMs) frequently hallucinate objects absent from the input image. We trace t...

Patient-centric radiology: Utilising large language models (LLMs) to improve patient communication and education

Purpose: To evaluate whether large language models (LLMs) can enhance clinician-patient communicatio...

See It, Say It, Sorted: An Iterative Training-Free Framework for Visually-Grounded Multimodal Reasoning in LVLMs

Recent large vision-language models (LVLMs) have demonstrated impressive reasoning ability by genera...

NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors

Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs incl...

Causal Decoding for Hallucination-Resistant Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet re...

Bridging Day and Night: Target-Class Hallucination Suppression in Unpaired Image Translation

Day-to-night unpaired image translation is important to downstream tasks but remains challenging due...

Representation Before Retrieval: Structured Patient Artifacts Reduce Hallucination in Clinical AI Systems

Background: Large language models show promise for clinical decision support, yet their propensity f...

Reproducible symptom subtypes of depression identified using unsupervised machine learning

Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, ind...

Development and validation of neurological health score using machine learning algorithms

Neurological health score (NHS), indicating the health of brain and nervous system, helps in identif...

HII-DPO: Eliminate Hallucination via Accurate Hallucination-Inducing Counterfactual Images

Large Vision-Language Models (VLMs) have achieved remarkable success across diverse multimodal tasks...

RSHallu: Dual-Mode Hallucination Evaluation for Remote-Sensing Multimodal Large Language Models with Domain-Tailored Mitigation

Multimodal large language models (MLLMs) are increasingly adopted in remote sensing (RS) and have sh...

Domain Adaptation Enables Cross-site Classification of First-episode Schizophrenia from Multimodal Neuroimaging Data

Identifying robust neuroimaging markers associated with schizophrenia is essential for advancing res...

ClueTracer: Question-to-Vision Clue Tracing for Training-Free Hallucination Suppression in Multimodal Reasoning

Large multimodal reasoning models solve challenging visual problems via explicit long-chain inferenc...

Brain neuromarkers predict self- and other-related mentalizing across adult, clinical, and developmental samples

Human social interactions rely on the ability to reflect on one's own and others' internal states an...

Individual Differences in Dopaminergic Modulation of Exploration-Exploitation Behaviour

Dopamine (DA) has been implicated in exploration-exploitation behaviour, i.e., exploring novel, pote...

Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework

Despite progress in Large Vision Language Models (LVLMs), object hallucination remains a critical is...

V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answering

Multimodal Large Language Models (MLLMs) have shown remarkable capability in assisting disease diagn...

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HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs

The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and ...

Supervision-by-Hallucination-and-Transfer: A Weakly-Supervised Approach for Robust and Precise Facial Landmark Detection

High-precision facial landmark detection (FLD) relies on high-resolution deep feature representation...

ELITE: Efficient Gaussian Head Avatar from a Monocular Video via Learned Initialization and TEst-time Generative Adaptation

We introduce ELITE, an Efficient Gaussian head avatar synthesis from a monocular video via Learned I...

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