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Paper Title: LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume Assessments

Zhaoyang Jiang, Zhizhong Fu, David McAllister, Yunsoo Kim, Honghan Wu

Abstract

Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such as Alzheimer's disease assessment. However, current deep-learning tools fragment this process: classifiers reduce a scan to a label, volumetric pipelines produce uninterpreted measurements, and vision-language models (VLMs) may generate fluent but potentially hallucinated conclusions. We present LoV3D, a pipeline for training 3D vision-language models, which reads longitudinal T1-weighted brain MRI, produces a region-level anatomical assessment, conducts longitudinal comparison with the prior scan, and finally outputs a three-class diagnosis (Cognitively Normal, Mild Cognitive Impairment, or Dementia) along with a synthesized diagnostic summary. The stepped pipeline grounds the final diagnosis by enforcing label consistency, longitudinal coherence, and biological plausibility, thereby reducing the risks of hallucinations. The training process introduces a clinically-weighted Verifier that scores candidate outputs automatically against normative references derived from standardized volume metrics, driving Direct Preference Optimization without a single human annotation. On a subject-level held-out ADNI test set (479 scans, 258 subjects), LoV3D achieves 93.7% three-class diagnostic accuracy (+34.8% over the no-grounding baseline), 97.2% on two-class diagnosis accuracy (+4% over the SOTA) and 82.6% region-level anatomical classification accuracy (+33.1% over VLM baselines). Zero-shot transfer yields 95.4% on MIRIAD (100% Dementia recall) and 82.9% three-class accuracy on AIBL, confirming high generalizability across sites, scanners, and populations. Code is available at https://github.com/Anonymous-TEVC/LoV-3D.

Paper Title: LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume Assessments

Abstract

Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such as Alzheimer's disease assessment. However, current deep-learning tools fragment this process: classifiers reduce a scan to a label, volumetric pipelines produce uninterpreted measurements, and vision-language models (VLMs) may generate fluent but potentially hallucinated conclusions. We present LoV3D, a pipeline for training 3D vision-language models, which reads longitudinal T1-weighted brain MRI, produces a region-level anatomical assessment, conducts longitudinal comparison with the prior scan, and finally outputs a three-class diagnosis (Cognitively Normal, Mild Cognitive Impairment, or Dementia) along with a synthesized diagnostic summary. The stepped pipeline grounds the final diagnosis by enforcing label consistency, longitudinal coherence, and biological plausibility, thereby reducing the risks of hallucinations. The training process introduces a clinically-weighted Verifier that scores candidate outputs automatically against normative references derived from standardized volume metrics, driving Direct Preference Optimization without a single human annotation. On a subject-level held-out ADNI test set (479 scans, 258 subjects), LoV3D achieves 93.7% three-class diagnostic accuracy (+34.8% over the no-grounding baseline), 97.2% on two-class diagnosis accuracy (+4% over the SOTA) and 82.6% region-level anatomical classification accuracy (+33.1% over VLM baselines). Zero-shot transfer yields 95.4% on MIRIAD (100% Dementia recall) and 82.9% three-class accuracy on AIBL, confirming high generalizability across sites, scanners, and populations. Code is available at https://github.com/Anonymous-TEVC/LoV-3D.
Paper Structure (16 sections, 4 equations, 1 figure, 3 tables)

This paper contains 16 sections, 4 equations, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Architecture Overview
  4. Verifiable Output Design: multi-task learning
  5. Normative Z-Score Model
  6. Clinically-Weighted Verifier
  7. Verifier-Guided Preference Optimization
  8. Training Pipeline
  9. Experiments
  10. Setup
  11. Baseline Comparison
  12. Ablation Study: Stage-Wise Analysis
  13. External Validation
  14. MIRIAD (binary: AD vs HC).
  15. AIBL (three-class: CN vs MCI vs Dementia).
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Model paradigms and the LoV3D training pipeline. Top: Published MRI classifiers produce only a two-class diagnostic label from MRI inputs. Middle and Bottom: multimodal VLM models that take a MRI and text prompt, and produce an output including (3-class) classification and a detailed LLM output. Specifically, Middle: 3D medical VLMs (RadFM, M3D-LaMed) and an ungrounded ablation (same pipeline, encoder warmed via diagnosis classification instead of regional regression). Bottom: LoV3D training introduces a three-stage learning paradigm to ground the VLM's cognitive prognosis reasoning. Stage 0 warms the encoder via a regional volume regression task; Stages 1a--b learn structured clinical reasoning through projector alignment and LoRA SFT for a multi-task learning (selecting relevant brain regions; classifying region deterioration categories; the longitudinal progression classification); Stage 2 refines outputs via Verifier-guided DPO (dashed border marks optimized modules) for a more reasonable diagnostic summary generation. LoV3D produces both a classification result and structured LLM output (right) comprising imaging observations, chain-of-thought reasoning, verifiable facts, and a diagnostic summary. Fire/snowflake icons denote trainable/frozen modules.