Schizophrenia

3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems

Volume denoising is a foundational problem in computational imaging, as many 3D imaging inverse problems face high levels of measurement noise. Inspired by the strong 2D image denoising properties of Field of Junctions (ICCV 2021), we propose a novel, fully volumetric 3D Field of Junctions (3D FoJ) representation that optimizes a junction of 3D wedges that best explain each 3D patch of a full volu...

AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have adopted visual token pruning strategies to mitigate substantial computational overhead incurred by extensive visual token sequences. While prior works primarily focus on either attention-based or diversity-based pruning methods, in-depth analysis of these approaches' characteristics and limitations remains largely unexplored. In this work, we conduct thoro...

Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering

Automated radiology report generation using vision-language models (VLMs) is limited by the risk of prior-comparison hallucination, where the model ge...

Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verification

Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies, generating diagnost...

Onco-Shikshak: An AI-Native Adaptive Learning Ecosystem for Medical Oncology Education

Medical oncology education faces a dual crisis: knowledge velocity that outpaces static curricula and large language model (LLM) risks hallucination a...

Disentangling Symptom Heterogeneity in Large-Scale Psychiatric Text: Domain-Adapted vs. Instruction-Tuned Transformers

Psychiatric disorders are fundamentally challenged by symptom heterogeneity, high comorbidity, and the absence of objective biomarkers, which together...

Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction

Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained gener...

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 this failure to spatial credit collapse: activation...

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 communication by simplifying radiology reports to improve pati...

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 generating long chain-of-thought (CoT) responses. Howeve...

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 include objects that do not appear in the input image....

Causal Decoding for Hallucination-Resistant Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet remain susceptible to object hallucination (introduc...

Continual-NExT: A Unified Comprehension And Generation Continual Learning Framework

Dual-to-Dual MLLMs refer to Multimodal Large Language Models, which can enable unified multimodal comprehension and generation through text and image ...

LGD-Net: Latent-Guided Dual-Stream Network for HER2 Scoring with Task-Specific Domain Knowledge

It is a critical task to evalaute HER2 expression level accurately for breast cancer evaluation and targeted treatment therapy selection. However, the...

A Large-Scale Computer-Vision Mapping of the Geometric Structures of Stroboscopically-Induced Visual Hallucinations

Visual hallucinations (VHs) occur across psychedelic states and diverse psychiatric and neurological conditions, yet their phenomenology remains diffi...

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 to large appearance shifts and the lack of direct...

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 for hallucination--generating plausible but unsuppo...

Reproducible symptom subtypes of depression identified using unsupervised machine learning

Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different sympt...

VIGIL: Tackling Hallucination Detection in Image Recontextualization

We introduce VIGIL (Visual Inconsistency & Generative In-context Lucidity), the first benchmark dataset and framework providing a fine-grained categor...

MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs

We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world...