Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection

• URL: http://arxiv.org/abs/2408.13978v1
• Date: Mon, 26 Aug 2024 01:54:37 GMT
• Title: Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection
• Authors: Qiuli Wang, Yongxu Liu, Li Ma, Xianqi Wang, Wei Chen, Xiaohong Yao,
• Abstract summary: 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
• Score: 9.68135211016703
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 on immunohistochemistry (IHC) staining techniques, utilizing markers such as CD20 for B cells. Despite the specificity of IHC, Hematoxylin-Eosin (H&E) staining offers a more accessible and cost-effective choice. Capitalizing on the prevalence of H&E staining slides, 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, such as nucleus, red blood cells, positive reaction regions, without explicit label information, and adeptly synthesizes realistic IHC-like virtual staining patches, even replicating the positive reaction. Further, we propose the Virtual IHC Pathology Analysis Network (VIPA-Net), an integrated framework encompassing a Mask-Guided Transfer Module and an H&E-Based Virtual Staining TLS Detection Module. VIPA-Net synergistically harnesses both H\&E staining slides and the synthesized virtual IHC patches to enhance the detection of TLSs within H&E Whole Slide Images (WSIs). We evaluate the network with a comprehensive dataset comprising 1019 annotated slides from The Cancer Genome Atlas (TCGA). Experimental results compellingly illustrate that the VIPA-Net substantially elevates TLS detection accuracy, effectively circumventing the need for actual CD20 staining across the public dataset.

Related papers

• Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency [2.063403009505468]
  Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) allows the automatic production of other useful IHC stains without the expensive physical staining process. Current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture, and color at tile boundaries. We propose a novel consistent WSI synthesis network, CC-WSI-Net, that extends GAN models to
  arXiv  Detail & Related papers  (2024-10-01T21:02:16Z)
• Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining [4.42401958204836]
  We propose a Pathological Semantics-Preserving Learning method for Virtual Staining. PSPStain incorporates the molecular-level semantic information and enhances semantics interaction. PSPStain outperforms current state-of-the-art H&E-to-IHC virtual staining methods.
  arXiv  Detail & Related papers  (2024-07-04T05:54:00Z)
• 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)
• Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon [1.741980945827445]
  Hematoxylin and Eosin (H&E) staining is one of the most frequently used stains for disease analysis, diagnosis, and grading. Pathologists do need differentchemical (IHC) stains to analyze specific structures or cells. Hematoxylin and Eosin (H&E) staining is one of the most frequently used stains for disease analysis, diagnosis, and grading.
  arXiv  Detail & Related papers  (2023-08-25T05:24:23Z)
• Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System [69.40329819373954]
  The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. We propose a boundary guided semantic learning network (BSNet) in this paper.
  arXiv  Detail & Related papers  (2022-09-07T05:01:38Z)
• Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions [68.41088365582831]
  White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. Most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient parcellation of 198 SWM clusters from whole-brain tractography.
  arXiv  Detail & Related papers  (2022-07-18T23:07:53Z)
• Virtual stain transfer in histology via cascaded deep neural networks [2.309018557701645]
  We demonstrate a virtual stain transfer framework via a cascaded deep neural network (C-DNN) Unlike a single neural network structure which only takes one stain type as input to digitally output images of another stain type, C-DNN first uses virtual staining to transform autofluorescence microscopy images into H&E. We successfully transferred the H&E-stained tissue images into virtual PAS (periodic acid-Schiff) stain.
  arXiv  Detail & Related papers  (2022-07-14T00:43:18Z)
• 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)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)
• Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification [76.89093364969253]
  We propose to apply Few-Shot Learning to skin disease identification to address the extreme scarcity of training sample problem. Based on a detailed analysis, we propose the Query-Relative (QR) loss, which proves superior to Cross Entropy (CE) under episode training. We further strengthen the proposed QR loss with a novel adaptive hard margin strategy.
  arXiv  Detail & Related papers  (2020-04-21T00:57:11Z)

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.