Unifying VLM-Guided Flow Matching and Spectral Anomaly Detection for Interpretable Veterinary Diagnosis

2026-04-07

AI Summary

This study addresses the challenges of data scarcity and insufficient model trustworthiness in automated pneumothorax diagnosis in dogs by proposing a novel approach that integrates generative segmentation with first-principles statistical analysis. The method employs a vision-language model to guide iterative flow-matching optimization for pixel-level lesion segmentation, followed by spectral anomaly detection based on random matrix theory to identify eigenvalues significantly deviating from normal distributions as indicators of pathology. The work introduces the first publicly available canine pneumothorax dataset with pixel-level annotations, achieving high boundary-precision segmentation and high-sensitivity anomaly detection. This framework not only ensures diagnostic accuracy but also provides interpretable evidence grounded in statistical principles.

Abstract

Automatic diagnosis of canine pneumothorax is challenged by data scarcity and the need for trustworthy models. To address this, we first introduce a public, pixel-level annotated dataset to facilitate research. We then propose a novel diagnostic paradigm that reframes the task as a synergistic process of signal localization and spectral detection. For localization, our method employs a Vision-Language Model (VLM) to guide an iterative Flow Matching process, which progressively refines segmentation masks to achieve superior boundary accuracy. For detection, the segmented mask is used to isolate features from the suspected lesion. We then apply Random Matrix Theory (RMT), a departure from traditional classifiers, to analyze these features. This approach models healthy tissue as predictable random noise and identifies pneumothorax by detecting statistically significant outlier eigenvalues that represent a non-random pathological signal. The high-fidelity localization from Flow Matching is crucial for purifying the signal, thus maximizing the sensitivity of our RMT detector. This synergy of generative segmentation and first-principles statistical analysis yields a highly accurate and interpretable diagnostic system (source code is available at: https://github.com/Pu-Wang-alt/Canine-pneumothorax).

Problem

• canine pneumothorax
• data scarcity
• trustworthy models
• interpretable diagnosis
• anomaly detection

Innovation

• Vision-Language Model
• Flow Matching
• Random Matrix Theory
• Spectral Anomaly Detection
• Interpretable Diagnosis