• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 540
Next 10 →

Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer

by Laura E. Boucheron, B. S. Manjunath, Neal R. Harvey - IEEE International Conference on Acoustics Speech and Signal Processing , 2010
"... Many features used in the analysis of pathology imagery are inspired by grading features defined by clinical pathologists as important for diagnosis and characterization. A large majority of these features are features of cell nuclei; as such, there is often the desire to segment the imagery into in ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
into individual nuclei prior to feature extraction and further analysis. In this paper we present an analysis of the utility of imperfectly segmented cell nuclei for classification of H&E stained histopathology imagery of breast tissue. We show the object- and image-level classification performance using

PROCEEDINGS Open Access Automated classification of breast cancer morphology in histopathological images

by Ville Ojansivu, Nina Linder, Esa Rahtu, Matti Pietikäinen, Mikael Lundin, Heikki Joensuu, Johan Lundin
"... The morphology of a breast cancer tumour, as examined through an optical microscope, is currently assessed visually by the pathologist in parallel with making the can-cer diagnosis. The grade of differentiation, which describes how closely the morphology of the tumour resembles the corresponding hea ..."
Abstract - Add to MetaCart
The morphology of a breast cancer tumour, as examined through an optical microscope, is currently assessed visually by the pathologist in parallel with making the can-cer diagnosis. The grade of differentiation, which describes how closely the morphology of the tumour resembles the corresponding

Automated Malignancy Detection in Breast Histopathological Images

by Andrei Chekkoury, Parmeshwar Khurd, Jie Ni, Claus Bahlmann, Ali Kamen, Amar Patel, Leo Grady, Maneesh Singh, Martin Groher, Nassir Navab, Elizabeth Krupinski, Jeffrey Johnson, Anna Graham, Ronald Weinstein
"... Detection of malignancy from histopathological images of breast cancer is a labor-intensive and error-prone process. To streamline this process, we present an efficient Computer Aided Diagnostic system that can differentiate between cancerous and non-cancerous H&E (hemotoxylin&eosin) biopsy ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
Detection of malignancy from histopathological images of breast cancer is a labor-intensive and error-prone process. To streamline this process, we present an efficient Computer Aided Diagnostic system that can differentiate between cancerous and non-cancerous H&E (hemotoxylin&eosin) biopsy

Multispectral Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology

by unknown authors , 2014
"... Accurate detection of mitosis plays a critical role in breast cancer histopathology. Manual detection and counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Multispectral imaging is a recent medical imag-ing technology, proven successful in increasing the ..."
Abstract - Add to MetaCart
Accurate detection of mitosis plays a critical role in breast cancer histopathology. Manual detection and counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Multispectral imaging is a recent medical imag-ing technology, proven successful in increasing

1 On the Classification of Imbalanced Datasets

by Arun Kumar, M. N H. S. Sheshadri
"... In recent research the classifications of imbalanced data sets have received considerable attention. It is natural that due to the class imbalance the classifier tends to favour majority class. In this paper we investigate the performance of different methods for handling data imbalance in the micro ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
for early detection of breast cancer. In this paper, we review in brief the state of the art techniques in the framework of imbalanced data sets and investigate the performance of different methods for microcalcification classification.

Regenerative Random Forest with Automatic Feature Selection to Detect Mitosis in Histopathological Breast Cancer Images

by Angshuman Paul, Anisha Dey, Dipti Prasad Mukherjee, Vijaya Tourani
"... Abstract. We propose a fast and accurate method for counting the mitotic figures from histopathological slides using regenerative random forest. Our method performs automatic feature selection in an integrated manner with classification. The proposed random forest assigns a weight to each feature (d ..."
Abstract - Add to MetaCart
Abstract. We propose a fast and accurate method for counting the mitotic figures from histopathological slides using regenerative random forest. Our method performs automatic feature selection in an integrated manner with classification. The proposed random forest assigns a weight to each feature

Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

by Shivang Naik, Scott Doyle, Shannon Agner, Anant Madabhushi, Michael Feldman, John Tomaszewski - In IEEE International Symposium on Biomedical Imaging: From Nano to Macro , 2008
"... Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a method-ology for automated detection and segmentation of structures of interest in digitized histopathology im ..."
Abstract - Cited by 32 (4 self) - Add to MetaCart
Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a method-ology for automated detection and segmentation of structures of interest in digitized histopathology

Weakly Supervised Histopathology Cancer Image Segmentation and Classification

by Yan Xua, Jun-yan Zhuc, Eric I-chao Changb, Maode Laid, Zhuowen Tue
"... Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tis-sues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed m ..."
Abstract - Add to MetaCart
Labeling a histopathology image as having cancerous regions or not is a critical task in cancer diagnosis; it is also clinically important to segment the cancer tis-sues and cluster them into various classes. Existing supervised approaches for image classification and segmentation require detailed

Automated Classification of Local Patches in Colon Histopathology ∗

by Habil Kalkan, Marius Nap, Robert P. W. Duin, Marco Loog
"... An automated histology analysis is proposed for classification of local image patches of colon histopathology images into four principle classes: nor-mal, cancer, adenomatous and inflamed classes. Shape features based on stroma, lumen and imperfectly seg-mented nuclei are combined with texture featu ..."
Abstract - Add to MetaCart
An automated histology analysis is proposed for classification of local image patches of colon histopathology images into four principle classes: nor-mal, cancer, adenomatous and inflamed classes. Shape features based on stroma, lumen and imperfectly seg-mented nuclei are combined with texture

Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features

by Scott Doyle, Shannon Agner, Anant Madabhushi, Michael Feldman, John Tomaszewski - in: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro , 2008
"... In this paper we present a novel image analysis methodology for au-tomatically distinguishing low and high grades of breast cancer from digitized histopathology. A set of over 3,400 image features, includ-ing textural and nuclear architecture based features, are extracted from a database of 48 breas ..."
Abstract - Cited by 26 (5 self) - Add to MetaCart
In this paper we present a novel image analysis methodology for au-tomatically distinguishing low and high grades of breast cancer from digitized histopathology. A set of over 3,400 image features, includ-ing textural and nuclear architecture based features, are extracted from a database of 48
Next 10 →
Results 1 - 10 of 540
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University