[2603.03527] Logit-Level Uncertainty Quantification in Vision-Language Models for Histopathology Image Analysis

Computer Science > Machine Learning arXiv:2603.03527 (cs) [Submitted on 3 Mar 2026] Title:Logit-Level Uncertainty Quantification in Vision-Language Models for Histopathology Image Analysis Authors:Betul Yurdem, Ferhat Ozgur Catak, Murat Kuzlu, Mehmet Kemal Gullu View a PDF of the paper titled Logit-Level Uncertainty Quantification in Vision-Language Models for Histopathology Image Analysis, by Betul Yurdem and 3 other authors View PDF HTML (experimental) 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-m...