32 research outputs found
Context-based Normalization of Histological Stains using Deep Convolutional Features
While human observers are able to cope with variations in color and
appearance of histological stains, digital pathology algorithms commonly
require a well-normalized setting to achieve peak performance, especially when
a limited amount of labeled data is available. This work provides a fully
automated, end-to-end learning-based setup for normalizing histological stains,
which considers the texture context of the tissue. We introduce Feature Aware
Normalization, which extends the framework of batch normalization in
combination with gating elements from Long Short-Term Memory units for
normalization among different spatial regions of interest. By incorporating a
pretrained deep neural network as a feature extractor steering a pixelwise
processing pipeline, we achieve excellent normalization results and ensure a
consistent representation of color and texture. The evaluation comprises a
comparison of color histogram deviations, structural similarity and measures
the color volume obtained by the different methods.Comment: In: 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA
2017
Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis
A novel deep autoencoder architecture is proposed for the analysis of histopathology images. Its purpose is to produce a disentangled latent representation in which the structure and colour information are confined to different subspaces so that stain-independent models may be learned. For this, we introduce two constraints on the representation which are implemented as a classifier and an adversarial discriminator. We show how they can be used for learning a latent representation across haematoxylin-eosin and a number of immune stains. Finally, we demonstrate the utility of the proposed representation in the context of matching image patches for registration applications and for learning a bag of visual words for whole slide image summarization
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
Variational Bayesian Blind Color Deconvolution of Histopathological Images
2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.Most whole-slide histological images are stained with two or more chemical dyes. Slide stain separation or color deconvolution is a crucial step within the digital pathology workflow. In this paper, the blind color deconvolution problem is formulated within the Bayesian framework. Starting from a multi-stained histological image, our model takes into account both spatial relations among the concentration image pixels and similarity between a given reference color-vector matrix and the estimated one. Using Variational Bayes inference, three efficient new blind color deconvolution methods are proposed which provide automated procedures to estimate all the model parameters in the problem. A comparison with classical and current state-of-the-art color deconvolution algorithms using real images has been carried out demonstrating the superiority of the proposed approach.Visual Information Processing (Ref. TIC-116
Artifact-Robust Graph-Based Learning in Digital Pathology
Whole slide images~(WSIs) are digitized images of tissues placed in glass
slides using advanced scanners. The digital processing of WSIs is challenging
as they are gigapixel images and stored in multi-resolution format. A common
challenge with WSIs is that perturbations/artifacts are inevitable during
storing the glass slides and digitizing them. These perturbations include
motion, which often arises from slide movement during placement, and changes in
hue and brightness due to variations in staining chemicals and the quality of
digitizing scanners. In this work, a novel robust learning approach to account
for these artifacts is presented. Due to the size and resolution of WSIs and to
account for neighborhood information, graph-based methods are called for. We
use graph convolutional network~(GCN) to extract features from the graph
representing WSI. Through a denoiser {and pooling layer}, the effects of
perturbations in WSIs are controlled and the output is followed by a
transformer for the classification of different grades of prostate cancer. To
compare the efficacy of the proposed approach, the model without denoiser is
trained and tested with WSIs without any perturbation and then different
perturbations are introduced in WSIs and passed through the network with the
denoiser. The accuracy and kappa scores of the proposed model with prostate
cancer dataset compared with non-robust algorithms show significant improvement
in cancer diagnosis