Deep learning assessment of breast terminal duct lobular unit involution: towards automated prediction of breast cancer risk.
Suzanne C. WetsteinAllison M. OnkenChristina LuffmanGabrielle M. BakerMichael E. PyleKevin H. KenslerYing LiuBart BakkerRuud VluttersMarinus B. van LeeuwenLaura C. CollinsStuart J. SchnittJosien P. W. PluimRulla M. TamimiYujing J. HengMitko VetaPublished in: CoRR (2019)
Keyphrases
- breast cancer
- deep learning
- breast cancer diagnosis
- breast cancer detection
- computer aided detection
- computer aided diagnosis
- breast tissue
- mammogram images
- survival analysis
- bladder cancer
- risk assessment
- benign and malignant
- outcome prediction
- digital mammography
- cancer treatment
- dce mri
- cad systems
- unsupervised learning
- machine learning
- logistic regression
- diagnosis of breast cancer
- early detection of breast cancer
- weakly supervised
- cancer patients
- mammographic images
- mental models
- pattern recognition
- computer aided
- data mining
- x ray
- semi supervised
- high dimensional
- training set
- image segmentation
- decision trees
- image processing
- decision making
- feature selection
- computer vision