Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features

Wan, Tao; Cao, Junhui; Chen, Jianhui; Qin, Zengchang

doi:10.1016/j.neucom.2016.05.084

Cited by 106 publications

(56 citation statements)

References 57 publications

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“…One of them classified the cases as either glioblastoma multiform (grade IV) or lower grade glioma (grade II and III), with an accuracy of 96%, and the other discriminated grade II glioma from grade III with an accuracy of 71%. For the breast biopsy images, a CNN‐based model distinguished low‐, intermediate‐, and high‐grade breast cancers with an accuracy of 69% [42]. DL‐based models were also set up to discriminate normal tissue, low‐grade and high‐grade colorectal adenocarcinoma with an accuracy of 91% [22].…”

Section: Application Of Dl‐based Ai In Tumor Pathologymentioning

confidence: 99%

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Jiang

Yue

Wang

et al. 2020

Cancer Communications

238

147

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The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

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Section: Application Of Dl‐based Ai In Tumor Pathologymentioning

confidence: 99%

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Jiang

Yue

Wang

et al. 2020

Cancer Communications

238

147

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“…-Haralick texture features [10,7,11,14,13,15,2]; -Gabor filter [7,4,26]; -Local Binary Pattern (LBP) [22,25]; -First-order histogram statistics [4,15,16,20].…”

Section: Methodsologymentioning

confidence: 99%

Automated Detection of Segmental Glomerulosclerosis in Kidney Histopathology

Araújo¹,

Schnitman²,

Duarte³

et al. 2018

Proceeding XIII Brazilian Congress on Computational Inteligence

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This paper presents the current results in the detection of segmental glomerulosclerosis by analyzing histological images of kidney biopsies, stained using hematoxylin and eosin (H&E) or periodic acid-Schiff (PAS) techniques. The work is part of the development of the PathoSpotter-K system, which aims the detection of elemental lesions in histological images of kidney. Currently, PathoSpotter-K accuracy for detecting segmental glomerulosclerosis is 84.8% for H&E stained samples and 81.3% for PAS stained samples. Such rates are similar to that reported for most of the analogous systems used for histological lesions detection in other organs and diseases.

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“…The scoring is based on size, shape and texture of segmented cell nuclei. Wan et al proposed multi-level features based approach for grading breast cancer histopathology images [10]. Hybrid active contour-based segmentation model is used for segmenting different nuclei in histopathology image.…”

Section: Related Workmentioning

confidence: 99%

BREAST CANCER GRADING OF H&E STAINED HISTOPATHOLOGY IMAGES

Mane¹,

Tagalpallewar²

2018

IJIVP

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Breast cancer is the common existing form of cancers amongst women. The automatic image analysis methods have an enormous potential to decrease the workload in a pathology laboratory. The grading of breast cancer histopathology images is used to find the level of breast cancer. The automatic grading of breast cancer histopathology images is a challenging task. In this paper a system for automatic detection of breast cancer grading of H&E stained histopathological images is presented. An image processing techniques such as preprocessing, segmentation, feature extraction and classification are used in this system. The segmentation of nuclei in H&E stained image is performed using color thresholding and maximum entropy thresholding. The features are computed according to Bloom Richardson grading criteria. The decision tree classifier is used to classify input image into three group i.e., low grade, intermediate grade and high grade.

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Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features

Cited by 106 publications

References 57 publications

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Emerging role of deep learning‐based artificial intelligence in tumor pathology

Automated Detection of Segmental Glomerulosclerosis in Kidney Histopathology

BREAST CANCER GRADING OF H&E STAINED HISTOPATHOLOGY IMAGES

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