TY - GEN
T1 - Breast density classification with convolutional neural networks
AU - Fonseca, Pablo
AU - Castañeda, Benjamin
AU - Valenzuela, Ricardo
AU - Wainer, Jacques
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Breast Density Classification is a problem in Medical Imaging domain that aims to assign an American College of Radiology’s BIRADS category (I-IV) to a mammogram as an indication of tissue density. This is performed by radiologists in an qualitative way, and thus subject to variations from one physician to the other. In machine learning terms it is a 4-ordered-classes classification task with highly unbalance training data, as classes are not equally distributed among populations, even with variations among ethnicities. Deep Learning techniques in general became the state-of-the-art for many imaging classification tasks, however, dependent on the availability of large datasets. This is not often the case for Medical Imaging, and thus we explore Transfer Learning and Dataset Augmentationn. Results show a very high squared weighted kappa score of 0.81 (0.95 C.I. [0.77,0.85]) which is high in comparison to the 8 medical doctors that participated in the dataset labeling 0.82 (0.95 CI [0.77, 0.87]).
AB - Breast Density Classification is a problem in Medical Imaging domain that aims to assign an American College of Radiology’s BIRADS category (I-IV) to a mammogram as an indication of tissue density. This is performed by radiologists in an qualitative way, and thus subject to variations from one physician to the other. In machine learning terms it is a 4-ordered-classes classification task with highly unbalance training data, as classes are not equally distributed among populations, even with variations among ethnicities. Deep Learning techniques in general became the state-of-the-art for many imaging classification tasks, however, dependent on the availability of large datasets. This is not often the case for Medical Imaging, and thus we explore Transfer Learning and Dataset Augmentationn. Results show a very high squared weighted kappa score of 0.81 (0.95 C.I. [0.77,0.85]) which is high in comparison to the 8 medical doctors that participated in the dataset labeling 0.82 (0.95 CI [0.77, 0.87]).
UR - http://www.scopus.com/inward/record.url?scp=85013427486&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-52277-7_13
DO - 10.1007/978-3-319-52277-7_13
M3 - Conference contribution
AN - SCOPUS:85013427486
SN - 9783319522760
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 101
EP - 108
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 21st Iberoamerican Congress, CIARP 2016, Proceedings
A2 - Beltran-Castanon, Cesar
A2 - Famili, Fazel
A2 - Nystrom, Ingela
PB - Springer Verlag
T2 - 21st Iberoamerican Congress on Pattern Recognition, CIARP 2016
Y2 - 8 November 2016 through 11 November 2016
ER -