TY - GEN
T1 - A comparative study of CNN, BoVW and LBP for classification of histopathological images
AU - Kumar, Meghana Dinesh
AU - Babaie, Morteza
AU - Zhu, Shujin
AU - Kalra, Shivam
AU - Tizhoosh, H. R.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/2
Y1 - 2018/2/2
N2 - Despite the progress made in the field of medical imaging, it remains a large area of open research, especially due to the variety of imaging modalities and disease-specific characteristics. This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images. We introduce a new dataset, KIMIA Path960, that contains 960 histopathology images belonging to 20 different classes (different tissue types). We make this dataset publicly available. The small size of the dataset and its inter-and intra-class variability makes it ideal for initial investigations when comparing image descriptors for search and classification in complex medical imaging cases like histopathology. We investigate deep features, LBP histograms and BoVW to classify the images via leave-one-out validation. The accuracy of image classification obtained using LBP was 90.62% while the highest accuracy using deep features reached 94.72%. The dictionary approach (BoVW) achieved 96.50%. Deep solutions may be able to deliver higher accuracies but they need extensive training with a large number of (balanced) image datasets.
AB - Despite the progress made in the field of medical imaging, it remains a large area of open research, especially due to the variety of imaging modalities and disease-specific characteristics. This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images. We introduce a new dataset, KIMIA Path960, that contains 960 histopathology images belonging to 20 different classes (different tissue types). We make this dataset publicly available. The small size of the dataset and its inter-and intra-class variability makes it ideal for initial investigations when comparing image descriptors for search and classification in complex medical imaging cases like histopathology. We investigate deep features, LBP histograms and BoVW to classify the images via leave-one-out validation. The accuracy of image classification obtained using LBP was 90.62% while the highest accuracy using deep features reached 94.72%. The dictionary approach (BoVW) achieved 96.50%. Deep solutions may be able to deliver higher accuracies but they need extensive training with a large number of (balanced) image datasets.
KW - LBP
KW - bag-of-visual words
KW - deep features
KW - deep networks
KW - histopathology
KW - image classification
KW - image retrieval
UR - http://www.scopus.com/inward/record.url?scp=85046154709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046154709&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2017.8285162
DO - 10.1109/SSCI.2017.8285162
M3 - Conference contribution
AN - SCOPUS:85046154709
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 7
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -