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540
Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer
- 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 ..."
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Cited by 3 (0 self)
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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
"... 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 ..."
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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
"... 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 ..."
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Cited by 2 (0 self)
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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
, 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 ..."
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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
"... 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 ..."
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Cited by 2 (0 self)
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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
"... 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 ..."
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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
- 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 ..."
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Cited by 32 (4 self)
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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
"... 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 ..."
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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 ∗
"... 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 ..."
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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
- 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 ..."
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Cited by 26 (5 self)
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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
Results 1 - 10
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540
