scholarly journals Deep learning-based research on the influence of training data size for breast cancer pathology detection

2019 ◽  
Vol 2019 (23) ◽  
pp. 8729-8732
Author(s):  
Chongyang Cui ◽  
Shangchun Fan ◽  
Han Lei ◽  
Xiaolei Qu ◽  
Dezhi Zheng
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Training Data ◽  
Cancer Pathology ◽  
Breast Cancer Pathology

Mammaglobin-A tissue expression, breast cancer pathology and survival

2017 ◽  
Vol 43 (5) ◽  
pp. S52
Author(s):  
Liz Baker ◽  
Louise Hall ◽  
Naomi Whiteoak ◽  
Lucy Hill ◽  
Deborah Wilson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tissue Expression ◽  
Cancer Pathology ◽  
Breast Cancer Pathology

P91 From initial breast cancer visit to hospitalisation: taking care of women with breast cancer pathology

2010 ◽  
Vol 14 ◽  
pp. S51-S52
Author(s):  
L. Rubio ◽  
B. Rossetti ◽  
F. Didier ◽  
A. Maldifassi ◽  
P. Arnaboldi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Pathology ◽  
Breast Cancer Pathology ◽  
Taking Care

scholarly journals Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer

Tomography ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 131-141
Author(s):  
Kanae Takahashi ◽  
Tomoyuki Fujioka ◽  
Jun Oyama ◽  
Mio Mori ◽  
Emi Yamaga ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Image Classification ◽  
Test Data ◽  
Characteristic Curve ◽  
Maximum Intensity Projection ◽  
Maximum Intensity ◽  
Training Data ◽  
Ct Image ◽  
Pet Ct

Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with multiple degrees of PET maximum-intensity projection (MIP) images contributes to increase diagnostic accuracy for PET/CT image classification in BC. We retrospectively gathered 400 images of 200 BC and 200 non-BC patients for training data. For each image, we obtained PET MIP images with four different degrees (0°, 30°, 60°, 90°) and made two DL models using Xception. One DL model diagnosed BC with only 0-degree MIP and the other used four different degrees. After training phases, our DL models analyzed test data including 50 BC and 50 non-BC patients. Five radiologists interpreted these test data. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our 4-degree model, 0-degree model, and radiologists had a sensitivity of 96%, 82%, and 80–98% and a specificity of 80%, 88%, and 76–92%, respectively. Our 4-degree model had equal or better diagnostic performance compared with that of the radiologists (AUC = 0.936 and 0.872–0.967, p = 0.036–0.405). A DL model similar to our 4-degree model may lead to help radiologists in their diagnostic work in the future.

scholarly journals Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

2020 ◽  
Vol 6 (10) ◽  
pp. 101
Author(s):  
Mauricio Alberto Ortega-Ruiz ◽  
Cefa Karabağ ◽  
Victor García Garduño ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Morphological Features ◽  
Training Data ◽  
Morphological Operations ◽  
Morphological Parameters ◽  
Learning Approaches ◽  
Residual Cancer Burden ◽  
Histological Images ◽  
Treated Breast

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches.

Has breast cancer pathology and surgical management within an institution changed over a 9-year period?

2011 ◽  
Vol 37 (11) ◽  
pp. 1001
Author(s):  
Michelle Chin I. Lo ◽  
B. Hariri ◽  
T. Gandamihardja ◽  
G. Pattni ◽  
K. Hogben
Keyword(s):  
Breast Cancer ◽  
Surgical Management ◽  
Cancer Pathology ◽  
Breast Cancer Pathology

Concordance with breast cancer pathology reporting practice guidelines

2003 ◽  
Vol 196 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Neal W Wilkinson ◽  
Azin Shahryarinejad ◽  
Janet S Winston ◽  
Nancy Watroba ◽  
Stephen B Edge
Keyword(s):  
Breast Cancer ◽  
Practice Guidelines ◽  
Reporting Practice ◽  
Cancer Pathology ◽  
Pathology Reporting ◽  
Breast Cancer Pathology
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