199 research outputs found
GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis
Histopathological cancer diagnosis is based on visual examination of stained
tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely
employed worldwide. It is easy to acquire and cost effective, but cells and
tissue components show low-contrast with varying tones of dark blue and pink,
which makes difficult visual assessments, digital image analysis, and
quantifications. These limitations can be overcome by IHC staining of target
proteins of the tissue slide. IHC provides a selective, high-contrast imaging
of cells and tissue components, but their use is largely limited by a
significantly more complex laboratory processing and high cost. 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. This
data-driven method requires only a limited amount of labelled data but will
generate pixel level segmentation results. The proposed cCGAN model improves
the original network \cite{zhu_unpaired_2017} by adding category conditions and
introducing two structural loss functions, which realize a multi-subdomain
translation and improve the translation accuracy as well. % need to give
reasons here. Experiments demonstrate that the proposed model outperforms the
original method in unpaired image translation with multi-subdomains. We also
explore the potential of unpaired images to image translation method applied on
other histology images related tasks with different staining techniques
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Stain variation is a phenomenon observed when distinct pathology laboratories
stain tissue slides that exhibit similar but not identical color appearance.
Due to this color shift between laboratories, convolutional neural networks
(CNNs) trained with images from one lab often underperform on unseen images
from the other lab. Several techniques have been proposed to reduce the
generalization error, mainly grouped into two categories: stain color
augmentation and stain color normalization. The former simulates a wide variety
of realistic stain variations during training, producing stain-invariant CNNs.
The latter aims to match training and test color distributions in order to
reduce stain variation. For the first time, we compared some of these
techniques and quantified their effect on CNN classification performance using
a heterogeneous dataset of hematoxylin and eosin histopathology images from 4
organs and 9 pathology laboratories. Additionally, we propose a novel
unsupervised method to perform stain color normalization using a neural
network. Based on our experimental results, we provide practical guidelines on
how to use stain color augmentation and stain color normalization in future
computational pathology applications.Comment: Accepted in the Medical Image Analysis journa
Generative deep learning in digital pathology workflows
Funding: Supported by the Sir James Mackenzie Institute for Early Diagnosis, University of St Andrews and Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (grant number TS/S013121/1).Many modern histopathology laboratories are in the process of digitising their workflows. Once images of the tissue exist as digital data, it becomes feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on Deep Learning, promise systems that can identify pathologies in slide images with a high degree of accuracy. Generative modelling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology including the removal of color and intensity artefacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses some future directions for generative models within histopathology.PostprintPeer reviewe
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
Generative AI has received substantial attention in recent years due to its
ability to synthesize data that closely resembles the original data source.
While Generative Adversarial Networks (GANs) have provided innovative
approaches for histopathological image analysis, they suffer from limitations
such as mode collapse and overfitting in discriminator. Recently, Denoising
Diffusion models have demonstrated promising results in computer vision. These
models exhibit superior stability during training, better distribution
coverage, and produce high-quality diverse images. Additionally, they display a
high degree of resilience to noise and perturbations, making them well-suited
for use in digital pathology, where images commonly contain artifacts and
exhibit significant variations in staining. In this paper, we present a novel
approach, namely ViT-DAE, which integrates vision transformers (ViT) and
diffusion autoencoders for high-quality histopathology image synthesis. This
marks the first time that ViT has been introduced to diffusion autoencoders in
computational pathology, allowing the model to better capture the complex and
intricate details of histopathology images. We demonstrate the effectiveness of
ViT-DAE on three publicly available datasets. Our approach outperforms recent
GAN-based and vanilla DAE methods in generating realistic images.Comment: Submitted to MICCAI 202
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
Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images
The variation in histologic staining between different medical centers is one
of the most profound challenges in the field of computer-aided diagnosis. The
appearance disparity of pathological whole slide images causes algorithms to
become less reliable, which in turn impedes the wide-spread applicability of
downstream tasks like cancer diagnosis. Furthermore, different stainings lead
to biases in the training which in case of domain shifts negatively affect the
test performance. Therefore, in this paper we propose MultiStain-CycleGAN, a
multi-domain approach to stain normalization based on CycleGAN. Our
modifications to CycleGAN allow us to normalize images of different origins
without retraining or using different models. We perform an extensive
evaluation of our method using various metrics and compare it to commonly used
methods that are multi-domain capable. First, we evaluate how well our method
fools a domain classifier that tries to assign a medical center to an image.
Then, we test our normalization on the tumor classification performance of a
downstream classifier. Furthermore, we evaluate the image quality of the
normalized images using the Structural similarity index and the ability to
reduce the domain shift using the Fr\'echet inception distance. We show that
our method proves to be multi-domain capable, provides the highest image
quality among the compared methods, and can most reliably fool the domain
classifier while keeping the tumor classifier performance high. By reducing the
domain influence, biases in the data can be removed on the one hand and the
origin of the whole slide image can be disguised on the other, thus enhancing
patient data privacy.Comment: 19 pages, 11 figures, 3 table
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