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Logit-Level Uncertainty Quantification in Vision-Language Models for Histopathology Image Analysis

arXiv:2603.03527v1 Announce Type: new Abstract: Vision-Language Models (VLMs) with their multimodal capabilities have demonstrated remarkable success in almost all domains, including education, transportation, healthcare, energy, finance, law, and retail. Nevertheless, the utilization of VLMs in healthcare applications raises crucial concerns due to the sensitivity of large-scale medical data and the trustworthiness of these models (reliability, transparency, and security). This study proposes a logit-level uncertainty quantification (UQ) framework for histopathology image analysis using VLMs to deal with these concerns. UQ is evaluated for three VLMs using metrics derived from temperature-controlled output logits. The proposed framework demonstrates a critical separation in uncertainty behavior. While VLMs show high stochastic sensitivity (cosine similarity (CS) $<0.71$ and $<0.84$, Jensen-Shannon divergence (JS) $<0.57$ and $<0.38$, and Kullback-Leibler divergence (KL) $<0.55$ and $

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Betul Yurdem, Ferhat Ozgur Catak, Murat Kuzlu, Mehmet Kemal Gullu · · 1 min read · 10 views
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arXiv:2603.03527v1 Announce Type: new Abstract: Vision-Language Models (VLMs) with their multimodal capabilities have demonstrated remarkable success in almost all domains, including education, transportation, healthcare, energy, finance, law, and retail. Nevertheless, the utilization of VLMs in healthcare applications raises crucial concerns due to the sensitivity of large-scale medical data and the trustworthiness of these models (reliability, transparency, and security). This study proposes a logit-level uncertainty quantification (UQ) framework for histopathology image analysis using VLMs to deal with these concerns. UQ is evaluated for three VLMs using metrics derived from temperature-controlled output logits. The proposed framework demonstrates a critical separation in uncertainty behavior. While VLMs show high stochastic sensitivity (cosine similarity (CS) $<0.71$ and $<0.84$, Jensen-Shannon divergence (JS) $<0.57$ and $<0.38$, and Kullback-Leibler divergence (KL) $<0.55$ and $<0.35$, respectively for mean values of VILA-M3-8B and LLaVA-Med v1.5), near-maximal temperature impacts ($\Delta_T \approx 1.00$), and displaying abrupt uncertainty transitions, particularly for complex diagnostic prompts. In contrast, the pathology-specific PRISM model maintains near-deterministic behavior (mean CS $>0.90$, JS $<0.10$, KL $<0.09$) and significantly minimal temperature effects across all prompt complexities. These findings emphasize the importance of logit-level uncertainty quantification to evaluate trustworthiness in histopathology applications utilizing VLMs.

Executive Summary

The article proposes a logit-level uncertainty quantification framework for histopathology image analysis using Vision-Language Models (VLMs), addressing concerns of reliability, transparency, and security in healthcare applications. The framework evaluates three VLMs, demonstrating significant differences in uncertainty behavior, with the pathology-specific PRISM model showing near-deterministic behavior and minimal temperature effects. The study highlights the importance of uncertainty quantification in assessing trustworthiness in VLMs for histopathology applications.

Key Points

  • ▸ Proposed logit-level uncertainty quantification framework for VLMs in histopathology image analysis
  • ▸ Evaluation of three VLMs (VILA-M3-8B, LLaVA-Med v1.5, and PRISM) using temperature-controlled output logits
  • ▸ Significant differences in uncertainty behavior among the VLMs, with PRISM showing near-deterministic behavior

Merits

Novel Framework

The proposed framework provides a novel approach to uncertainty quantification in VLMs, addressing a critical concern in healthcare applications.

Comprehensive Evaluation

The study evaluates multiple VLMs using various metrics, providing a comprehensive understanding of their uncertainty behavior.

Demerits

Limited Generalizability

The study focuses on histopathology image analysis, which may limit the generalizability of the findings to other healthcare applications.

Lack of Clinical Validation

The study does not provide clinical validation of the proposed framework, which is essential for its adoption in real-world healthcare settings.

Expert Commentary

The article contributes significantly to the growing body of research on uncertainty quantification in AI systems, particularly in healthcare applications. The proposed framework provides a valuable tool for evaluating the trustworthiness of VLMs in histopathology image analysis. However, further research is needed to validate the framework clinically and to explore its generalizability to other healthcare applications. The study's findings also highlight the need for regulatory frameworks that address the concerns of reliability, transparency, and security in AI systems for healthcare.

Recommendations

  • ✓ Further research is needed to clinically validate the proposed framework and to explore its generalizability to other healthcare applications.
  • ✓ Regulatory frameworks should be developed to address the concerns of reliability, transparency, and security in AI systems for healthcare, including the use of uncertainty quantification and explainability metrics.

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