Schizophrenia

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

IMPORTANCE: Cognitive functioning is associated with various factors, such as age, sex, education, and childhood adversity, and is impaired in people with psychosis. In addition to specific effects of the disorder, cognitive impairments may reflect a greater exposure to general risk factors for poor cognition.

Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning.

PURPOSE: The neurobiological heterogeneity present in schizophrenia remains poorly understood. This likely contributes to the limited success of existing treatments and the observed variability in treatment responses. Our objective was to employ magnetic resonance imaging (MRI) and machine learning (ML) algorithms to improve the classification of schizophrenia and its subtypes.

Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.

INTRODUCTION: The diagnostic boundaries between schizophrenia and bipolar disorder are controversial due to the ambiguity of psychiatric nosology. Fro...

Towards a Systematic Evaluation of Hallucinations in Large-Vision Language Models

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance in complex multimodal tasks. However, these models still suffer from h...

Is Your Text-to-Image Model Robust to Caption Noise?

In text-to-image (T2I) generation, a prevalent training technique involves utilizing Vision Language Models (VLMs) for image re-captioning. Even tho...

An End-to-End Depth-Based Pipeline for Selfie Image Rectification

Portraits or selfie images taken from a close distance typically suffer from perspective distortion. In this paper, we propose an end-to-end deep le...

MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models

Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications, yet their propensity for hallucinations -- generating ...

Extract Free Dense Misalignment from CLIP

Recent vision-language foundation models still frequently produce outputs misaligned with their inputs, evidenced by object hallucination in caption...

Multimodal Preference Data Synthetic Alignment with Reward Model

Multimodal large language models (MLLMs) have significantly advanced tasks like caption generation and visual question answering by integrating visu...

AlzheimerRAG: Multimodal Retrieval Augmented Generation for PubMed articles

Recent advancements in generative AI have flourished the development of highly adept Large Language Models (LLMs) that integrate diverse data types ...

Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage

Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations. Our analysis reveals that ex...

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains chal...

Query pipeline optimization for cancer patient question answering systems

Retrieval-augmented generation (RAG) mitigates hallucination in Large Language Models (LLMs) by using query pipelines to retrieve relevant external ...

Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation

Direct Preference Optimization (DPO) has been demonstrated to be highly effective in mitigating hallucinations in Large Vision Language Models (LVLM...

A MapReduce Approach to Effectively Utilize Long Context Information in Retrieval Augmented Language Models

While holding great promise for improving and facilitating healthcare, large language models (LLMs) struggle to produce up-to-date responses on evol...

ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports

The increasing adoption of AI-generated radiology reports necessitates robust methods for detecting hallucinations--false or unfounded statements th...

RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models

Understanding and addressing corner cases is essential for ensuring the safety and reliability of autonomous driving systems. Vision-language models...

Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios

Large vision-language models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation tasks. However, these mode...

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

While large vision-language models (LVLMs) have shown impressive capabilities in generating plausible responses correlated with input visual content...

Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder

For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) h...