Schizophrenia

Hugan Tiaoshen Formula Improves the Comorbid Mechanism of Schizophrenia and Sleep Disorder via Multitarget Interaction Network.

This study aims to integrate cross-disease omics data and perform multidimensional analysis to uncover the molecular basis of schizophrenia (SCZ) and sleep disorder (SD) comorbidity and to systematically analyze the potential mechanism of the Hugan Tiaoshen Formula (HGTS) in treating SCZ with SD. Integrate transcriptional data of SCZ and SD from the GEO database, screen disease-shared differential...

Do LLMs Dream of Discrete Algorithms?

Large Language Models (LLMs) have rapidly transformed the landscape of artificial intelligence, enabling natural language interfaces and dynamic orchestration of software components. However, their reliance on probabilistic inference limits their effectiveness in domains requiring strict logical reasoning, discrete decision-making, and robust interpretability. This paper investigates these limit...

LLM-Assisted Question-Answering on Technical Documents Using Structured Data-Aware Retrieval Augmented Generation

Large Language Models (LLMs) are capable of natural language understanding and generation. But they face challenges such as hallucination and outdat...

HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation

Recent progress in vision-language segmentation has significantly advanced grounded visual understanding. However, these models often exhibit halluc...

Mitigating Hallucination of Large Vision-Language Models via Dynamic Logits Calibration

Large Vision-Language Models (LVLMs) have demonstrated significant advancements in multimodal understanding, yet they are frequently hampered by hal...

Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models

Recent advancements in multimodal large language models have enhanced document understanding by integrating textual and visual information. However,...

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement

Vision-Language Models (VLMs) now generate discourse-level, multi-sentence visual descriptions, challenging text scene graph parsers originally desi...

Abstract Meaning Representation for Hospital Discharge Summarization

The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly imp...

MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model

Multimodal medical imaging integrates diverse data types, such as structural and functional neuroimaging, to provide complementary insights that enh...

Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning

Evaluating foundation models for crystallographic reasoning requires benchmarks that isolate generalization behavior while enforcing physical constr...

GM-LDM: Latent Diffusion Model for Brain Biomarker Identification through Functional Data-Driven Gray Matter Synthesis

Generative models based on deep learning have shown significant potential in medical imaging, particularly for modality transformation and multimoda...

Not All Tokens and Heads Are Equally Important: Dual-Level Attention Intervention for Hallucination Mitigation

Large vision-language models (LVLMs) have shown remarkable capabilities across a wide range of multimodal tasks. However, they remain prone to visua...

DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models

This paper presents DiffFuSR, a modular pipeline for super-resolving all 12 spectral bands of Sentinel-2 Level-2A imagery to a unified ground sampli...

Stop learning it all to mitigate visual hallucination, Focus on the hallucination target

Multimodal Large Language Models (MLLMs) frequently suffer from hallucination issues, generating information about objects that are not present in i...

HalLoc: Token-level Localization of Hallucinations for Vision Language Models

Hallucinations pose a significant challenge to the reliability of large vision-language models, making their detection essential for ensuring accura...

Text-Aware Image Restoration with Diffusion Models

Image restoration aims to recover degraded images. However, existing diffusion-based restoration methods, despite great success in natural image res...

SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding

Despite significant advancements in Vision-Language Models (VLMs), the performance of existing VLMs remains hindered by object hallucination, a crit...

A Pilot Analysis Investigating the Use of AI in Malingering.

Generative artificial intelligence (AI), with its increasing ubiquity and power, will likely transform forensic psychiatry, sparking both advances and...

MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models

The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologi...

Decoding the Structure-Activity Relationship of the Dopamine D3 Receptor-Selective Ligands Using Machine and Deep Learning Approaches.

Dysfunctions of the dopamine D2 and D3 receptors (D2 and D3) are implicated in neuropsychiatric conditions such as Parkinson's disease, schizophrenia,...