Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
- 6 June 2018
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 19 references indexed in Scilit:
- Digital image analysis in breast pathology—from image processing techniques to artificial intelligenceTranslational Research, 2017
- 3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification ResultsPublished by Cold Spring Harbor Laboratory ,2017
- Classification of breast cancer histology images using Convolutional Neural NetworksPLOS ONE, 2017
- Breast cancer histopathological image classification using Convolutional Neural NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Deep learning for visual understanding: A reviewNeurocomputing, 2016
- Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy SpecimensJAMA, 2015
- Gradient boosting machines, a tutorialFrontiers in Neurorobotics, 2013
- Breast carcinoma malignancy grading by Bloom–Richardson system vs proliferation index: reproducibility of grade and advantages of proliferation indexLaboratory Investigation, 2005
- pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long‐term follow‐upHistopathology, 1991