Psychiatry

Schizophrenia

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

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DetailVerifyBench: A Benchmark for Dense Hallucination Localization in Long Image Captions

Accurately detecting and localizing hallucinations is a critical task for ensuring high reliability ...

Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction

Metal artifacts from high-attenuation implants severely degrade CT image quality, obscuring critical...

HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models

Large vision-language models can produce object hallucinations in image descriptions, highlighting t...

EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors

Vision-Language Models (VLMs) excel at multimodal tasks, but they remain vulnerable to hallucination...

Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation

As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than a...

How and why does deep ensemble coupled with transfer learning increase performance in bipolar disorder and schizophrenia classification?

Transfer learning (TL) and deep ensemble learning (DE) have recently been shown to outperform simple...

Evaluating the Large Language Model-Based Quality Assurance Tool for Auto-Contouring

Purpose: Manual verification of AI-based auto-contouring is labor-intensive and prone to fatigue-rel...

VaaS is a Multi-Layer Hallucination Reduction Pipeline for AI-Assisted Science: Production Validation and Prospective Benchmarking

The deployment of large language models (LLMs) for science carries an intrinsic risk: hallucination ...

Pan-Pharmacological Drug-Target Interaction Prediction with 3D-Informed Protein Encoding at Scale

Accurate prediction of drug-target binding affinity across multiple pharmacological endpoints remain...

Finding Distributed Object-Centric Properties in Self-Supervised Transformers

Self-supervised Vision Transformers (ViTs) like DINO show an emergent ability to discover objects, t...

Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery

Recent work has questioned whether large language models (LLMs) can perform genuine in-context learn...

Generative Score Inference for Multimodal Data

Accurate uncertainty quantification is crucial for making reliable decisions in various supervised l...

ClipTTT: CLIP-Guided Test-Time Training Helps LVLMs See Better

Large vision-language models (LVLMs) tend to hallucinate, especially when visual inputs are corrupte...

Revealing Multi-View Hallucination in Large Vision-Language Models

Large vision-language models (LVLMs) are increasingly being applied to multi-view image inputs captu...

Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification

Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability ...

To Agree or To Be Right? The Grounding-Sycophancy Tradeoff in Medical Vision-Language Models

Vision-language models (VLMs) adapted to the medical domain have shown strong performance on visual ...

FontCrafter: High-Fidelity Element-Driven Artistic Font Creation with Visual In-Context Generation

Artistic font generation aims to synthesize stylized glyphs based on a reference style. However, exi...

FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs

Multimodal Large Language Models (MLLMs) suffer from hallucinations. Existing hallucination evaluati...

Beyond AI Psychosis and Sycophancy: Structural Drift as a System-Level Safety Failure

Background: Conversational AI safety systems are primarily evaluated using message-level content mon...

To See or To Please: Uncovering Visual Sycophancy and Split Beliefs in VLMs

When VLMs answer correctly, do they genuinely rely on visual information or exploit language shortcu...

CycleCap: Improving VLMs Captioning Performance via Self-Supervised Cycle Consistency Fine-Tuning

Visual-Language Models (VLMs) have achieved remarkable progress in image captioning, visual question...

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