scholarly journals Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images

2020 ◽  
Author(s):  
Samuel Ortega ◽  
Martin Halicek ◽  
Himar Fabelo ◽  
Raul Guerra ◽  
Carlos Lopez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Cells ◽  
Hyperspectral Imaging ◽  
Breast Cancer Cells ◽  
Histological Images

scholarly journals Forecast Breast Cancer Cells from Microscopic Biopsy Images using Big Transfer (BiT): A Deep Learning Approach

2021 ◽  
Vol 12 (10) ◽  
Author(s):  
Md. Ashiqul Islam ◽  
Dhonita Tripura ◽  
Mithun Dutta ◽  
Md. Nymur Rahman Shuvo ◽  
Wasik Ahmmed Fahim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Learning Approach

Raman hyperspectral imaging of transferrin-bound iron in T47D and MDA-MB-231 breast cancer cells

2020 ◽  
Author(s):  
Ting Chean Khoo ◽  
Kate Tubbesing ◽  
Alena Rudkouskaya ◽  
Georgios A. Athanassiadis ◽  
Anna V. Sharikova ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Hyperspectral Imaging ◽  
Breast Cancer Cells

scholarly journals Detection of live breast cancer cells in brightfield microscopy images containing white blood cells by image analysis and deep learning

2021 ◽  
Author(s):  
Golnaz Moallem ◽  
Adity A. Pore ◽  
Anirudh Gangadhar ◽  
Hamed Sari-Sarraf ◽  
Siva A Vanapalli
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Cells ◽  
Blood Cells ◽  
Breast Cancer Cells ◽  
White Blood Cells ◽  
Size Difference ◽  
Microscopy Images

Significance: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. Aim: The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright field microscopy images that contain white blood cells (WBCs). Approach: We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate cancer cells from WBCs. The second approach is based on Faster Region-based Convolutional Neural Network (Faster R-CNN). Results: Both approaches detected cancer cells with high sensitivity and specificity with the Faster R-CNN being more efficient and suitable for deployment. The distinctive feature used by the CNN used to discriminate is cell size, however, in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. Conclusions: CNN-based deep learning approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.

scholarly journals Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap

2020 ◽  
Vol 11 (6) ◽  
pp. 2976
Author(s):  
Sangyeon Youn ◽  
Kyungsu Lee ◽  
Jeehoon Son ◽  
In-Hwan Yang ◽  
Jae Youn Hwang
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Fully Automatic

The proliferation of breast cancer cells are inhibited by the human intrabody targeting cyclinD1

2010 ◽  
Vol 34 (8) ◽  
pp. S49-S49
Author(s):  
Lei Wang ◽  
Xun Zhou ◽  
Lihong Zhou ◽  
Yong Chen ◽  
Xun Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cells
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