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SA-GAN: Stain Acclimation Generative Adversarial Network for Histopathology Image Analysis
2021
Applied Sciences
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Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. ...
These stain variations present in histopathology images affect the accuracy of the CAD systems. ...
Additionally, different generative adversarial networks (GANs)- [27] based stain transfer techniques [28] [29] [30] [31] have been proposed for color normalization of histopathology images. ...
doi:10.3390/app12010288
fatcat:zyjmjf7bdfga7d4hlby33c34gu
Histopathological Stain Transfer using Style Transfer Network with Adversarial Loss
[article]
2020
arXiv
pre-print
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In this work, we present a novel approach for the stain normalization problem using fast neural style transfer coupled with adversarial loss. ...
In recent years, there has been a good amount of research done for image stain normalization to address this issue. ...
We propose to use neural style transfer with addition of adversarial loss for image stain normalization. ...
arXiv:2010.02659v1
fatcat:zgyfbffjk5bjtbuz5gjoyq66cy
Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis
[article]
2020
arXiv
pre-print
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In our proposed method, a Stain-to-Stain Translation (STST) approach is used to stain normalization for Hematoxylin and Eosin (H&E) stained histopathology images, which learns not only the specific color ...
This diversity in staining, in addition to Interpretive disparity among pathologists more is one of the main challenges in designing robust and flexible systems for automated analysis. ...
Babak Ehteshami Bejnordi, for his guidance, time and feedback on this paper. ...
arXiv:2002.00647v1
fatcat:lfzhoxc2pffa5lnekhjekgvr3m
Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images
2019
Frontiers in Bioengineering and Biotechnology
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Computational image analysis is one means for evaluating digitized histopathology specimens that can increase the reproducibility and reliability with which cancer diagnoses are rendered while simultaneously ...
that is appropriate for the entire set of whole-slide images. ...
Adversarial Adaptation for Target Domain The color normalization process makes it possible to perform the stain transfer from source domain to target domain on images directly. ...
doi:10.3389/fbioe.2019.00102
pmid:31158269
pmcid:PMC6529804
fatcat:g6qtz2zs6rbx3e4huot4ph76g4
Inter-Semantic Domain Adversarial in Histopathological Images
[article]
2022
arXiv
pre-print
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In medical applications, histopathological images are often associated with data shift and they are hardly available. ...
We then use domain adversarial methods to transfer data shift invariance from one dataset to another dataset with different semantics and show that domain adversarial methods are efficient inter-semantically ...
In order words, we question the transferability of the domain adversarial process across tasks and image semantics. ...
arXiv:2201.09041v1
fatcat:fxyxfrjhcbajhe2t3nixpajk5q
Unpaired Stain Transfer using Pathology-Consistent Constrained Generative Adversarial Networks
2021
IEEE Transactions on Medical Imaging
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In our work, we propose a novel adversarial learning method for effective Ki-67-stained image generation from corresponding H&E-stained image. ...
We believe that our method has significant potential in clinical virtual staining and advance the progress of computer-aided multi-staining histology image analysis. ...
For multi staining histopathological image analysis, the most important thing is ensuring the consistency of pathology. ...
doi:10.1109/tmi.2021.3069874
pmid:33784619
fatcat:6nwsocrxynemhe5t6husnxfvz4
Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2008.02101v1 fatcat:vx5b6y23yffyjbx4qft5awftz4Other Versions
Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues. ...
We integrate semantic information at different layers between a pre-trained semantic network and the stain color normalization network. ...
BenTaieb, A., Hamarneh, G.: Adversarial stain transfer for histopathology image analysis. IEEE Trans. Med. Imaging 37(3), 792-802 (2018) 10. ...
arXiv:2008.02101v3
fatcat:ywy3cavyb5fmjgddqgbhhc3tpa
Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning
[article]
2020
arXiv
pre-print
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To address the issue of color variations in histopathology images, this study proposes two stain style transfer models, SSIM-GAN and DSCSI-GAN, based on the generative adversarial networks. ...
Computational histopathology image diagnosis becomes increasingly popular and important, where images are segmented or classified for disease diagnosis by computers. ...
In this study, we develop a novel stain-style transfer framework combining a GAN network and a classification network for color normalization on histopathology images. ...
arXiv:2007.12578v1
fatcat:bwgrkk3zkrdy7fkkq35e7uat3y
Multi-stage domain adversarial style reconstruction for cytopathological image stain normalization
[article]
2019
arXiv
pre-print
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The different stain styles of cytopathological images have a negative effect on the generalization ability of automated image analysis algorithms. ...
This article proposes a new framework that normalizes the stain style for cytopathological images through a stain removal module and a multi-stage domain adversarial style reconstruction module. ...
ACKNOWLEDGMENT We thank the Optical Bioimaging Core Facility of WNLO HUST for the support in data acquisition. ...
arXiv:1909.05184v1
fatcat:vgscqavbarb45hsnmd2rnup6xq
Self Adversarial Attack as an Augmentation Method for Immunohistochemical Stainings
2021
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
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It has been shown that unpaired image-to-image translation methods constrained by cycle-consistency hide the information necessary for accurate input reconstruction as imperceptible noise. ...
We demonstrate that, when applied to histopathology data, this hidden noise appears to be related to stain specific features and show that this is the case with two immunohistochemical stainings during ...
STAIN TRANSFER SELF ADVERSARIAL ATTACK Given samples of two histopathological stains a ∼ A and b ∼ B, the goal is to learn two mappings (translators) G AB : a ∼ A → b ∼ B and G BA : b ∼ B → a ∼ A. ...
doi:10.1109/isbi48211.2021.9433838
fatcat:rsrfqotr3zaznfs2xcftkzttsa
Self adversarial attack as an augmentation method for immunohistochemical stainings
[article]
2021
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
It has been shown that unpaired image-to-image translation methods constrained by cycle-consistency hide the information necessary for accurate input reconstruction as imperceptible noise. ...
We demonstrate that, when applied to histopathology data, this hidden noise appears to be related to stain specific features and show that this is the case with two immunohistochemical stainings during ...
STAIN TRANSFER SELF ADVERSARIAL ATTACK Given samples of two histopathological stains a ∼ A and b ∼ B, the goal is to learn two mappings (translators) G AB : a ∼ A → b ∼ B and G BA : b ∼ B → a ∼ A. ...
arXiv:2103.11362v1
fatcat:j5ezybppt5bcvdfs7q4ub62tou
GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis
[article]
2019
arXiv
pre-print
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Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H&E) is a standard stain routinely employed worldwide. ...
We proposed a conditional CycleGAN (cCGAN) network to transform the H&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide. ...
Our contribution In this study, we explore the potential of unpaired image-to-image translation as "virtual staining" for histopathology image analysis. ...
arXiv:1901.04059v1
fatcat:chk2uqqmi5ewrkjfk2v3g4r5vm
Deep Learning for Virtual Histological Staining of Bright-Field Microscopic Images of Unlabeled Carotid Artery Tissue
2020
Molecular Imaging and Biology
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Histological analysis of artery tissue samples is a widely used method for diagnosis and quantification of cardiovascular diseases. ...
We trained a convolutional neural network to build maps between the unstained images and histologically stained images using a conditional generative adversarial network model. ...
The authors would like to acknowledge the instrumental and technical support of multimodal biomedical imaging experimental platform, Institute of Automation, Chinese Academy of Sciences. ...
doi:10.1007/s11307-020-01508-6
pmid:32514884
fatcat:ovpe73vutbdjfphyhl672hwdpm
MVIP 2020 Table of Contents
2020
2020 International Conference on Machine Vision and Image Processing (MVIP)
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High-Resolution Document Image Reconstruction from Video 38. Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis 39. ...
Gaussian Soft Margin Angular Loss for Face Recognition 8. An Ensemble Model for Human Posture Recognition 9. Image Colorization using Generative Adversarial Networks and Transfer Learning 10. ...
doi:10.1109/mvip49855.2020.9116904
fatcat:6v7rolxpkfh6jb2fg2bhd4ssuq
Learning Domain-Invariant Representations of Histological Images
2019
Frontiers in Medicine
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We carried out a comparative analysis with staining normalization and data augmentation on two different tasks: generalization to images acquired in unseen pathology labs for mitosis detection and generalization ...
The proposed framework for domain-adversarial training is able to improve generalization performances on top of conventional methods. ...
In conclusion, we proposed a domain-adversarial framework for training CNN models on histopathology images, and we made a comparative analysis against conventional preprocessing methods. ...
doi:10.3389/fmed.2019.00162
pmid:31380377
pmcid:PMC6646468
fatcat:amrgj5rljbeczpuva34qbce6fy
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