Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment

• URL: http://arxiv.org/abs/2508.16209v1
• Date: Fri, 22 Aug 2025 08:31:03 GMT
• Title: Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment
• Authors: Yijie Zhang, Cagatay Isil, Xilin Yang, Yuzhu Li, Anna Elia, Karin Atlan, William Dean Wallace, Nir Pillar, Aydogan Ozcan,
• Abstract summary: We present a deep learning-based virtual multiplexed immunostaining framework to simultaneously generate ERG and PanCK.<n>This technique is based on autofluorescence microscopy images label-free tissue sections.<n>Its output images closely match the histochemical staining counterparts (ERG, PanCK and H&E) of the same tissue sections.
• Score: 5.3528119597796655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Immunohistochemistry (IHC) has transformed clinical pathology by enabling the visualization of specific proteins within tissue sections. However, traditional IHC requires one tissue section per stain, exhibits section-to-section variability, and incurs high costs and laborious staining procedures. While multiplexed IHC (mIHC) techniques enable simultaneous staining with multiple antibodies on a single slide, they are more tedious to perform and are currently unavailable in routine pathology laboratories. Here, we present a deep learning-based virtual multiplexed immunostaining framework to simultaneously generate ERG and PanCK, in addition to H&E virtual staining, enabling accurate localization and interpretation of vascular invasion in thyroid cancers. This virtual mIHC technique is based on the autofluorescence microscopy images of label-free tissue sections, and its output images closely match the histochemical staining counterparts (ERG, PanCK and H&E) of the same tissue sections. Blind evaluation by board-certified pathologists demonstrated that virtual mIHC staining achieved high concordance with the histochemical staining results, accurately highlighting epithelial cells and endothelial cells. Virtual mIHC conducted on the same tissue section also allowed the identification and localization of small vessel invasion. This multiplexed virtual IHC approach can significantly improve diagnostic accuracy and efficiency in the histopathological evaluation of vascular invasion, potentially eliminating the need for traditional staining protocols and mitigating issues related to tissue loss and heterogeneity.

Related papers

• Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining [37.3879490506952]
  We propose a novel topology-aware framework for H&E-to-IHC virtual staining.<n>Specifically, we introduce a Topology-aware Consistency Matching mechanism that employs graph contrastive learning and topological perturbations.<n>Our method outperforms state-of-the-art approaches, achieving superior generation quality with higher clinical relevance.
  arXiv  Detail & Related papers  (2026-01-06T08:28:38Z)
• LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR [51.11296719862485]
  We propose a Contrastive Learning and Cross-Modal alignment framework for gadolinium-free cardiomyopathy screening using cine CMR sequences.<n>By aligning the latent spaces of cine CMR and Late Gadolinium Enhancement (LGE) sequences, our model encodes fibrosis-specific pathology into cine CMR embeddings.
  arXiv  Detail & Related papers  (2025-08-23T07:21:23Z)
• Score-based Diffusion Model for Unpaired Virtual Histology Staining [7.648204151998162]
  Hematoxylin and eosin (H&E) staining visualizes histology but lacks specificity for diagnostic markers.<n>Hematoxylin and eosin (H&E) staining provides protein-targeted staining but is restricted by tissue availability and antibody specificity.<n>Virtual staining, i.e., translating the H&E image to its IHC counterpart while preserving tissue structure, is promising for efficient IHC generation.<n>This study proposes a mutual-information (MI)-guided score-based diffusion model for unpaired virtual staining.
  arXiv  Detail & Related papers  (2025-06-29T11:02:45Z)
• CLAIM: Clinically-Guided LGE Augmentation for Realistic and Diverse Myocardial Scar Synthesis and Segmentation [3.052913696182197]
  We introduce CLAIM: textbfClinically-Guided textbfLGE textbfAugmentation for Realtextbfiiyocardial Scar Synthesis and framework.<n>At its core is the SMILE module, which conditions a diffusion-based generator on the clinically adopted AHA 17-segment model to synthesize images with anatomically consistent and spatially diverse scar patterns.<n> Experimental results show that CLAIM produces anatomically coherent scar patterns and achieves higher Dice similarity with real scar distributions compared to baseline models.
  arXiv  Detail & Related papers  (2025-06-18T15:21:34Z)
• Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining [3.24061990641619]
  The traditional histochemical staining process is time-consuming, costly, and labor-intensive.<n>We present a panel of virtual staining neural networks for lung and heart transplant biopsies.<n>Virtual staining networks consistently produce high-quality histology images with high color uniformity.
  arXiv  Detail & Related papers  (2024-09-09T00:18:48Z)
• Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection [9.68135211016703]
  Histological Tertiary Lymphoid Structures (TLSs) are increasingly recognized for their correlation with the efficacy of immunotherapy in various solid tumors. Traditionally, the identification and characterization of TLSs rely onchemistry (IHC) staining techniques, utilizing markers such as CD20 for B cells. We introduce a novel Mask-Guided Adversarial Transfer Learning method designed for virtual pathological staining. This method adeptly captures the nuanced color variations across diverse tissue types under various staining conditions, without explicit label information. We propose the Virtual IHC Pathology Analysis Network (VIPA-Net), an integrated framework encompassing a Mask-Guided
  arXiv  Detail & Related papers  (2024-08-26T01:54:37Z)
• Automated segmentation of rheumatoid arthritis immunohistochemistry stained synovial tissue [0.0]
  Rheumatoid Arthritis (RA) is a chronic, autoimmune disease which primarily affects the joint's synovial tissue. It is a highly heterogeneous disease, with wide cellular and molecular variability observed in synovial tissues. We train a UNET on a hand-curated, real-world multi-centre clinical dataset R4RA, which contains multiple types of IHC staining. The model obtains a DICE score of 0.865 and successfully segments different types of IHC staining, as well as dealing with variance in colours, intensity and common WSIs artefacts from the different clinical centres.
  arXiv  Detail & Related papers  (2023-09-13T18:43:14Z)
• Digital staining in optical microscopy using deep learning -- a review [47.86254766044832]
  Digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. We provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.
  arXiv  Detail & Related papers  (2023-03-14T15:23:48Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• 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)
• Deep learning-based transformation of the H&E stain into special stains [44.38127957263123]
  We show the utility of supervised learning-based computational stain transformation from H&E to different special stains using tissue sections from kidney needle core biopsies. Results: The quality of the special stains generated by the stain transformation network was statistically equivalent to those generated through standard histochemical staining.
  arXiv  Detail & Related papers  (2020-08-20T10:12:03Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)

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.