Pathology-Guided Virtual Staining Metric for Evaluation and Training

• URL: http://arxiv.org/abs/2507.12624v1
• Date: Wed, 16 Jul 2025 20:39:55 GMT
• Title: Pathology-Guided Virtual Staining Metric for Evaluation and Training
• Authors: Qiankai Wang, James E. D. Tweel, Parsin Haji Reza, Anita Layton,
• Abstract summary: PaPIS (Pathology-Aware Perceptual Image Similarity) is a novel FR-IQA metric specifically tailored for virtual staining evaluation.<n> PaPIS more accurately aligns with pathology-relevant visual cues and distinguishes subtle cellular structures.
• Score: 0.49998148477760973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining [2.030529002782949]
  We develop a virtual staining model based on conditional Generative Adversarial Networks.<n>We demonstrate to achieve heightened pathological fidelity through a blind pathological expert evaluation.
  arXiv  Detail & Related papers  (2025-11-24T09:56:35Z)
• PathoHR: Hierarchical Reasoning for Vision-Language Models in Pathology [3.459714932882085]
  Current vision-language (VL) models often struggle to capture the complex reasoning required for interpreting structured pathological reports.<n>We propose PathoHR-Bench, a novel benchmark designed to evaluate VL models' abilities in hierarchical semantic understanding and compositional reasoning within the pathology domain.<n>We further introduce a pathology-specific VL training scheme that generates enhanced and perturbed samples for multimodal contrastive learning.
  arXiv  Detail & Related papers  (2025-09-07T15:42:38Z)
• Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis [44.38638601819933]
  Current staging models for Diabetic Retinopathy (DR) are hardly interpretable.<n>We present a novel method that integrates graph representation learning with vision-language models (VLMs) to deliver explainable DR diagnosis.
  arXiv  Detail & Related papers  (2025-03-12T20:19:07Z)
• A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging [36.95712533471744]
  We introduce a general virtual staining framework that is adaptable to various conditions.<n>We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities.
  arXiv  Detail & Related papers  (2025-01-07T07:45:21Z)
• Pitfalls of topology-aware image segmentation [81.19923502845441]
  We identify critical pitfalls in model evaluation that include inadequate connectivity choices, overlooked topological artifacts, and inappropriate use of evaluation metrics.<n>We propose a set of actionable recommendations to establish fair and robust evaluation standards for topology-aware medical image segmentation methods.
  arXiv  Detail & Related papers  (2024-12-19T08:11:42Z)
• Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework [43.453943987647015]
  Medical vision language pre-training has emerged as a frontier of research, enabling zero-shot pathological recognition. Due to the complex semantics of biomedical texts, current methods struggle to align medical images with key pathological findings in unstructured reports. This is achieved by consulting a large language model and medical experts. Ours improves the accuracy of recent methods by up to 8.56% and 17.26% for seen and unseen categories, respectively.
  arXiv  Detail & Related papers  (2024-03-12T13:18:22Z)
• Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection [17.87825422578005]
  This study introduces the notion of a population-level context for pathology detection and employs a graph theoretic approach to model and incorporate it into the latent code of an autoencoder. PopuSense seeks to capture additional intra-group variations inherent in biomedical data that a local or global context of the convolutional model might miss or smooth out.
  arXiv  Detail & Related papers  (2024-03-04T18:44:30Z)
• Deepfake histological images for enhancing digital pathology [0.40631409309544836]
  We develop a generative adversarial network model that synthesizes pathology images constrained by class labels. We investigate the ability of this framework in synthesizing realistic prostate and colon tissue images. We extend the approach to significantly more complex images from colon biopsies and show that the complex microenvironment in such tissues can also be reproduced.
  arXiv  Detail & Related papers  (2022-06-16T17:11:08Z)
• Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology [5.164102666113966]
  We conduct a search for good representations in pathology by training a variety of self-supervised models with validation on a variety of weakly-supervised and patch-level tasks. Our key finding is in discovering that Vision Transformers using DINO-based knowledge distillation are able to learn data-efficient and interpretable features in histology images.
  arXiv  Detail & Related papers  (2022-03-01T16:14:41Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification [6.690876060631452]
  We propose a novel pipeline to build robust deep neural networks for AMR classification based on StyPath. Each image was generated in 1.84 + 0.03 seconds using a single GTX V TITAN and pytorch. Our results imply that our style-transfer augmentation technique improves histological classification performance.
  arXiv  Detail & Related papers  (2020-07-09T18:02:49Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.