Breast Cancer Histopathology Image Analysis: A Review

By M. Veta, Josien P. W. Pluim, P. Diest, Max A. Viergever. IEEE Transactions on Biomedical Engineering, 2014

652 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/473e14a05a557d826f745bc28aac7aacfe6de28f

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In Artificial Intelligence

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Artificial intelligence as the next step towards precision pathology (2020, background): “For a comprehensive review of object detection and segmentation, see Gurcan et al. and Veta et al. [50, 51].”
Assessment of algorithms for mitosis detection in breast cancer histopathology images (2014, neutral): “For a wider overview of histopathology image analysis techniques for breast cancer and other tissue types, we refer the reader to the recent reviews in Gurcan et al. (2009) and Veta et al. (2014).”
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases (2016, background): “[2,3] A number of image analysis tasks in DP involve some sort of quantification (e.g., cell or mitosis counting) or tissue grading (classification).”
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images (2015, neutral): “Hematoxylin and Eosin (H&E) are standard stains that highlight nuclei in blue/purple and cytoplasm in pink in order to visualize the structure of interest in the tissue [5].”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, background): “Accurate nuclei segmentation is a crucial step in cancer analysis and grading [68].”
Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images (2016, neutral): “Recently proposed techniques for nucleus detection in routine H&E histology images rely on morphological features such as symmetry and stability of the nuclear region to identify nuclei [12].”
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features (2017, background): “Histopathology image analysis is a gold standard for cancer recognition and diagnosis [1, 2].”
Classification of breast cancer histology images using Convolutional Neural Networks (2017, background): “For instance, the analysis of nuclei morphology may be sufficient to classify a tissue as benign or malignant [10].”
A Dataset for Breast Cancer Histopathological Image Classification (2016, background): “[14] point out that the main obstacle in the development of new histopathology”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “Broadly, there exist three popular strategies for nucleus/cell segmentation [18]: 1) separate the foreground from the background, and then, split the object clumps, if present, into individ-”
Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent (2017, background): “…in histopathology image analysis 13,17,27–29 , a set of hand-crafted features (color/intensity features, texture features, graph-based features, etc.) and machine learning methods (random forests and support vector machines) could and have been applied to histopathology image analysis 33,45–48 .”
Deep learning for magnification independent breast cancer histopathology image classification (2016, methodology): “addition to timely and costly process, such analysis can be hampered by pathologists different interpretations [3].”