123 research outputs found
Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance
© 2020, Springer Nature Switzerland AG. 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 propose a self-supervised approach to incorporate semantic guidance into a GAN based stain normalization framework and preserve detailed structural information. Our method does not require manual segmentation maps which is a significant advantage over existing methods. We integrate semantic information at different layers between a pre-trained semantic network and the stain color normalization network. The proposed scheme outperforms other color normalization methods leading to better classification and segmentation performance
MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss
Histopathology relies on the analysis of microscopic tissue images to
diagnose disease. A crucial part of tissue preparation is staining whereby a
dye is used to make the salient tissue components more distinguishable.
However, differences in laboratory protocols and scanning devices result in
significant confounding appearance variation in the corresponding images. This
variation increases both human error and the inter-rater variability, as well
as hinders the performance of automatic or semi-automatic methods. In the
present paper we introduce an unsupervised adversarial network to translate
(and hence normalize) whole slide images across multiple data acquisition
domains. Our key contributions are: (i) an adversarial architecture which
learns across multiple domains with a single generator-discriminator network
using an information flow branch which optimizes for perceptual loss, and (ii)
the inclusion of an additional feature extraction network during training which
guides the transformation network to keep all the structural features in the
tissue image intact. We: (i) demonstrate the effectiveness of the proposed
method firstly on H\&E slides of 120 cases of kidney cancer, as well as (ii)
show the benefits of the approach on more general problems, such as flexible
illumination based natural image enhancement and light source adaptation
A comparative evaluation of image-to-image translation methods for stain transfer in histopathology
Image-to-image translation (I2I) methods allow the generation of artificial
images that share the content of the original image but have a different style.
With the advances in Generative Adversarial Networks (GANs)-based methods, I2I
methods enabled the generation of artificial images that are indistinguishable
from natural images. Recently, I2I methods were also employed in histopathology
for generating artificial images of in silico stained tissues from a different
type of staining. We refer to this process as stain transfer. The number of I2I
variants is constantly increasing, which makes a well justified choice of the
most suitable I2I methods for stain transfer challenging. In our work, we
compare twelve stain transfer approaches, three of which are based on
traditional and nine on GAN-based image processing methods. The analysis relies
on complementary quantitative measures for the quality of image translation,
the assessment of the suitability for deep learning-based tissue grading, and
the visual evaluation by pathologists. Our study highlights the strengths and
weaknesses of the stain transfer approaches, thereby allowing a rational choice
of the underlying I2I algorithms. Code, data, and trained models for stain
transfer between H&E and Masson's Trichrome staining will be made available
online.Comment: 17 pages, 3 figures, 5 tables, accepted to Medical Imaging with Deep
Learning (MIDL) 2023, to be published in Proceedings of Machine Learning
Researc
Domain Generalization in Computational Pathology: Survey and Guidelines
Deep learning models have exhibited exceptional effectiveness in
Computational Pathology (CPath) by tackling intricate tasks across an array of
histology image analysis applications. Nevertheless, the presence of
out-of-distribution data (stemming from a multitude of sources such as
disparate imaging devices and diverse tissue preparation methods) can cause
\emph{domain shift} (DS). DS decreases the generalization of trained models to
unseen datasets with slightly different data distributions, prompting the need
for innovative \emph{domain generalization} (DG) solutions. Recognizing the
potential of DG methods to significantly influence diagnostic and prognostic
models in cancer studies and clinical practice, we present this survey along
with guidelines on achieving DG in CPath. We rigorously define various DS
types, systematically review and categorize existing DG approaches and
resources in CPath, and provide insights into their advantages, limitations,
and applicability. We also conduct thorough benchmarking experiments with 28
cutting-edge DG algorithms to address a complex DG problem. Our findings
suggest that careful experiment design and CPath-specific Stain Augmentation
technique can be very effective. However, there is no one-size-fits-all
solution for DG in CPath. Therefore, we establish clear guidelines for
detecting and managing DS depending on different scenarios. While most of the
concepts, guidelines, and recommendations are given for applications in CPath,
we believe that they are applicable to most medical image analysis tasks as
well.Comment: Extended Versio
The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review
Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis of different types of cancer. The preparation and digitization of histological tissues leads to the introduction of artifacts and variations that need to be addressed before the tissues are analyzed. WSI preprocessing can significantly improve the performance of computational pathology systems and is often used to facilitate human or machine analysis. Color preprocessing techniques are frequently mentioned in the literature, while other areas are usually ignored. In this paper, we present a detailed study of the state-of-the-art in three different areas of WSI preprocessing: Artifacts detection, color variation, and the emerging field of pathology-specific data augmentation. We include a summary of evaluation techniques along with a discussion of possible limitations and future research directions for new methods.European Commission 860627Ministerio de Ciencia e Innovacion (MCIN)/Agencia Estatal de Investigacion (AEI) PID2019-105142RB-C22Fondo Europeo de Desarrollo Regional (FEDER)/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades B-TIC-324-UGR20Instituto de Salud Carlos III
Spanish Government
European Commission BES-2017-08158
Generalized Zero Shot Learning For Medical Image Classification
In many real world medical image classification settings we do not have
access to samples of all possible disease classes, while a robust system is
expected to give high performance in recognizing novel test data. We propose a
generalized zero shot learning (GZSL) method that uses self supervised learning
(SSL) for: 1) selecting anchor vectors of different disease classes; and 2)
training a feature generator. Our approach does not require class attribute
vectors which are available for natural images but not for medical images. SSL
ensures that the anchor vectors are representative of each class. SSL is also
used to generate synthetic features of unseen classes. Using a simpler
architecture, our method matches a state of the art SSL based GZSL method for
natural images and outperforms all methods for medical images. Our method is
adaptable enough to accommodate class attribute vectors when they are available
for natural images
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