305 research outputs found
Two-tier tissue decomposition for histopathological image representation and classification
Cataloged from PDF version of article.In digital pathology, devising effective image representations is crucial to design
robust automated diagnosis systems. To this end, many studies have proposed to
develop object-based representations, instead of directly using image pixels, since
a histopathological image may contain a considerable amount of noise typically at
the pixel-level. These previous studies mostly define their objects, based on the
color information, as to approximately represent histological tissue components
in an image and then use the spatial distribution of these objects for image
representation and classification. Thus, object definition has a direct effect on the
way of representing the image, which in turn affects classification accuracies. In
this thesis, we present a new model for effective representation and classification
of histopathological images. The contributions of this model are twofold. First, it
introduces a new two-tier tissue decomposition method for defining a set of multityped
objects in an image. Different than the previous studies, these objects
are defined combining the texture, shape, and size information and they may
correspond to individual histological components as well as tissue sub-regions of
different characteristics. As its second contribution, it defines a new metric, which
we call “dominant blob scale”, to characterize the shape and size of an object
with a single scalar value. Our experiments on colon tissue images reveal that this
new object definition and characterization provides distinguishing representation
of normal and cancerous histopathological images, which is effective to obtain
more accurate classification results compared to its counterparts.Gültekin, TunçM.S
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification
In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structure. In this paper, we
propose an automatic feature discovery framework for extracting discriminative
class-specific features and present a low-complexity method for classification
and disease grading in histopathology. Essentially, our Discriminative
Feature-oriented Dictionary Learning (DFDL) method learns class-specific
features which are suitable for representing samples from the same class while
are poorly capable of representing samples from other classes. Experiments on
three challenging real-world image databases: 1) histopathological images of
intraductal breast lesions, 2) mammalian lung images provided by the Animal
Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor
images from The Cancer Genome Atlas (TCGA) database, show the significance of
DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging
(ISBI), 201
Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings
The classification of histopathological images is of great value in both
cancer diagnosis and pathological studies. However, multiple reasons, such as
variations caused by magnification factors and class imbalance, make it a
challenging task where conventional methods that learn from image-label
datasets perform unsatisfactorily in many cases. We observe that tumours of the
same class often share common morphological patterns. To exploit this fact, we
propose an approach that learns similarity-based multi-scale embeddings (SMSE)
for magnification-independent histopathological image classification. In
particular, a pair loss and a triplet loss are leveraged to learn
similarity-based embeddings from image pairs or image triplets. The learned
embeddings provide accurate measurements of similarities between images, which
are regarded as a more effective form of representation for histopathological
morphology than normal image features. Furthermore, in order to ensure the
generated models are magnification-independent, images acquired at different
magnification factors are simultaneously fed to networks during training for
learning multi-scale embeddings. In addition to the SMSE, to eliminate the
impact of class imbalance, instead of using the hard sample mining strategy
that intuitively discards some easy samples, we introduce a new reinforced
focal loss to simultaneously punish hard misclassified samples while
suppressing easy well-classified samples. Experimental results show that the
SMSE improves the performance for histopathological image classification tasks
for both breast and liver cancers by a large margin compared to previous
methods. In particular, the SMSE achieves the best performance on the BreakHis
benchmark with an improvement ranging from 5% to 18% compared to previous
methods using traditional features
Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification
This work was supported by project PID2019-105142RB-C22 funded by MCIN / AEI / 10.13039 / 501100011033, Spain, and project P20_00286 funded by FEDER /Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Spain. The work by Fernando Pérez-Bueno was sponsored by Ministerio de Economía, Industria y Competitividad , Spain, under FPI contract BES-2017-081584 . Funding for open access charge: Universidad de Granada / CBUA, Spain.Stain variation between images is a main issue in the analysis of histological images. These color variations, produced by different staining protocols and scanners in each laboratory, hamper the performance of computer-aided diagnosis (CAD) systems that are usually unable to generalize to unseen color distributions. Blind color deconvolution techniques separate multi-stained images into single stained bands that can then be used to reduce the generalization error of CAD systems through stain color normalization and/or stain color augmentation. In this work, we present a Bayesian modeling and inference blind color deconvolution framework based on the K-Singular Value Decomposition algorithm. Two possible inference procedures, variational and empirical Bayes are presented. Both provide the automatic estimation of the stain color matrix, stain concentrations and all model parameters. The proposed framework is tested on stain separation, image normalization, stain color augmentation, and classification problems.CBUAJunta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y UniversidadesFamily Process Institute
BES-2017-081584Universidad de GranadaEuropean Regional Development FundMinisterio de Economía, Industria y Competitividad, Gobierno de EspañaAgencia Estatal de Investigación
P20_0028
Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis
In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers
Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?
© 2018 Awan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 6-249-1-053. The content of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University
Constrained Delaunay triangulation for diagnosis and grading of colon cancer
Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 93-107.In our century, the increasing rate of cancer incidents makes it inevitable to employ
computerized tools that aim to help pathologists more accurately diagnose
and grade cancerous tissues. These mathematical tools offer more stable and
objective frameworks, which cause a reduced rate of intra- and inter-observer
variability. There has been a large set of studies on the subject of automated
cancer diagnosis/grading, especially based on textural and/or structural tissue
analysis. Although the previous structural approaches show promising results for
different types of tissues, they are still unable to make use of the potential information
that is provided by tissue components rather than cell nuclei. However,
this additional information is one of the major information sources for the tissue
types with differentiated components including luminal regions being useful to
describe glands in a colon tissue.
This thesis introduces a novel structural approach, a new type of constrained
Delaunay triangulation, for the utilization of non-nuclei tissue components. This
structural approach first defines two sets of nodes on cell nuclei and luminal
regions. It then constructs a constrained Delaunay triangulation on the nucleus
nodes with the lumen nodes forming its constraints. Finally, it classifies the
tissue samples using the features extracted from this newly introduced constrained
Delaunay triangulation.
Working with 213 colon tissues taken from 58 patients, our experiments
demonstrate that the constrained Delaunay triangulation approach leads to
higher accuracies of 87.83 percent and 85.71 percent for the training and test
sets, respectively. The experiments also show that the introduction of this new
structural representation, which allows definition of new features, provides a more
robust graph-based methodology for the examination of cancerous tissues and
better performance than its predecessors.Erdoğan, Süleyman TuncerM.S
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