WHO classification of tumours of the breast

By S. Lakhani. 2012

1,678 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a9dee07849a22cf3466681da5c825d62ed487c0d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Deep features for breast cancer histopathological image classification (2017, methodology): “Since histopathological analysis remains the most widely used method for BC diagnosis [2], and most of the diagnosis continues being done by pathologists applying visual inspection of histological samples under the microscope, automatic classification of histopathological images is a research topic ”
A Dataset for Breast Cancer Histopathological Image Classification (2016, background): “Diagnosis from a histopathology image is thus the gold standard in diagnosing almost all types of cancer, including BC [5], [6].”
A common classification framework for neuroendocrine neoplasms: an International Agency for Research on Cancer (IARC) and World Health Organization (WHO) expert consensus proposal (2018, methodology): “They classified NENs in the breast into (1) NETs, well-differentiated; these include low and intermediate-grade tumors, which by definition in the breast are malignant, and based on the presence of a peripheral myoepithelial cell layer they are classified and managed as either in situ or invasive di”
A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity. (2018, unknown): “(Lakhani, 2012).”
Breast cancer histopathological image classification using Convolutional Neural Networks (2016, methodology): “recent advances in the comprehension of the molecular biology of BC progression and the discovery of new related molecular markers, the histopathological analysis remains the most widely used method for BC diagnosis [2].”