{"ID":2894128,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12624","arxiv_id":"2507.12624","title":"Pathology-Guided Virtual Staining Metric for Evaluation and Training","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.","short_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 or...","url_abs":"https://arxiv.org/abs/2507.12624","url_pdf":"https://arxiv.org/pdf/2507.12624v1","authors":"[\"Qiankai Wang\",\"James E. D. Tweel\",\"Parsin Haji Reza\",\"Anita Layton\"]","published":"2025-07-16T20:39:55Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\",\"eess.SY\"]","methods":"[]","has_code":false}