Schizophrenia

Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training

In standard large vision-language models (LVLMs) pre-training, the model typically maximizes the joint probability of the caption conditioned on the image via next-token prediction (NTP); however, since only a small subset of caption tokens directly relates to the visual content, this naive NTP unintentionally fits the model to noise and increases the risk of hallucination. We present PRIOR, a s...

Symbolically-Guided Visual Plan Inference from Uncurated Video Data

Visual planning, by offering a sequence of intermediate visual subgoals to a goal-conditioned low-level policy, achieves promising performance on long-horizon manipulation tasks. To obtain the subgoals, existing methods typically resort to video generation models but suffer from model hallucination and computational cost. We present Vis2Plan, an efficient, explainable and white-box visual planni...

Multimodal Survival Modeling in the Age of Foundation Models

The Cancer Genome Atlas (TCGA) has enabled novel discoveries and served as a large-scale reference through its harmonized genomics, clinical, and im...

Critique Before Thinking: Mitigating Hallucination through Rationale-Augmented Instruction Tuning

Despite significant advancements in multimodal reasoning tasks, existing Large Vision-Language Models (LVLMs) are prone to producing visually ungrou...

TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking

In the age of social media, the rapid spread of misinformation and rumors has led to the emergence of infodemics, where false information poses a si...

Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models

Vision-Language Models (VLMs) are becoming increasingly popular in the medical domain, bridging the gap between medical images and clinical language...

Multimodal Integrated Knowledge Transfer to Large Language Models through Preference Optimization with Biomedical Applications.

The scarcity of high-quality multimodal biomedical data limits the ability to effectively fine-tune pretrained Large Language Models (LLMs) for specia...

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity wit...

Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning.

BACKGROUND AND OBJECTIVE: Accurate detection of schizophrenia poses a grand challenge as a complex and heterogeneous mental disorder. Current diagnost...

The Association Between Psychotropic Medications and Cognitive Functioning in a Real-World Cohort of 869 Individuals with Schizophrenia.

BACKGROUND: Cognitive deficits are a central feature of schizophrenia for which there are not any established pharmacological treatments. Antipsychoti...

Osiris: A Lightweight Open-Source Hallucination Detection System

Retrieval-Augmented Generation (RAG) systems have gained widespread adoption by application builders because they leverage sources of truth to enabl...

Mitigating Image Captioning Hallucinations in Vision-Language Models

Hallucinations in vision-language models (VLMs) hinder reliability and real-world applicability, usually stemming from distribution shifts between p...

Interpretable Zero-shot Learning with Infinite Class Concepts

Zero-shot learning (ZSL) aims to recognize unseen classes by aligning images with intermediate class semantics, like human-annotated concepts or cla...

Invoke Interfaces Only When Needed: Adaptive Invocation for Large Language Models in Question Answering

The collaborative paradigm of large and small language models (LMs) effectively balances performance and cost, yet its pivotal challenge lies in pre...

Regression is all you need for medical image translation

The acquisition of information-rich images within a limited time budget is crucial in medical imaging. Medical image translation (MIT) can help enha...

Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer

Accurate extraction of key information from 2D engineering drawings is crucial for high-precision manufacturing. Manual extraction is time-consuming...

Triggering Hallucinations in LLMs: A Quantitative Study of Prompt-Induced Hallucination in Large Language Models

Hallucinations in large language models (LLMs) present a growing challenge across real-world applications, from healthcare to law, where factual rel...

Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning.

IMPORTANCE: The diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence ...

Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs

Medical Large Multi-modal Models (LMMs) have demonstrated remarkable capabilities in medical data interpretation. However, these models frequently g...

Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data

Mental and cognitive representations are believed to reside on low-dimensional, non-linear manifolds embedded within high-dimensional brain activity...