# Pathology-Guided Virtual Staining Metric for Evaluation and Training

arXiv: https://arxiv.org/abs/2507.12624 · HTML: https://mlsanity.com/paper/2507.12624/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022)
- [A study of why we need to reassess full reference image quality assessment with medical images](https://mlsanity.com/paper/2405.19097/) (2024)
- [A study on the adequacy of common IQA measures for medical images](https://mlsanity.com/paper/2405.19224/) (2024)
- [Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks](https://mlsanity.com/paper/2407.08625/) (2024)
- [Objective quality assessment of medical images and videos: Review and challenges](https://mlsanity.com/paper/2212.07396/) (2022)
- [Automated Whole Slide Imaging for Label-Free Histology using Photon Absorption Remote Sensing Microscopy](https://mlsanity.com/paper/2304.13736/) (2023)
- [Multi-Channel Feature Extraction for Virtual Histological Staining of Photon Absorption Remote Sensing Images](https://mlsanity.com/paper/2307.01824/) (2023)
- [A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging](https://mlsanity.com/paper/2501.03592/) (2025)
- [Virtual Histology with Photon Absorption Remote Sensing using a Cycle-Consistent Generative Adversarial Network with Weakly Registered Pairs](https://mlsanity.com/paper/2306.08583/) (2023)

## Built on by (most cited first, 1 of 1)

- [HAPS: Rethinking Image Similarity for Virtual Staining](https://mlsanity.com/paper/2605.20362/) (2026)

## Abstract (arXiv)

Virtual staining has emerged as a powerful alternative to traditional histopathological staining techniques, enabling rapid, reagent-free image transformations. However, existing evaluation methods predominantly rely on full-reference image quality assessment (FR-IQA) metrics such as structural similarity, which are originally designed for natural images and often fail to capture pathology-relevant features. Expert pathology reviews have also been used, but they are inherently subjective and time-consuming. In this study, we introduce PaPIS (Pathology-Aware Perceptual Image Similarity), a novel FR-IQA metric specifically tailored for virtual staining evaluation. PaPIS leverages deep learning-based features trained on cell morphology segmentation and incorporates Retinex-inspired feature decomposition to better reflect histological perceptual quality. Comparative experiments demonstrate that PaPIS more accurately aligns with pathology-relevant visual cues and distinguishes subtle cellular structures that traditional and existing perceptual metrics tend to overlook. Furthermore, integrating PaPIS as a guiding loss function in a virtual staining model leads to improved histological fidelity. This work highlights the critical need for pathology-aware evaluation frameworks to advance the development and clinical readiness of virtual staining technologies.