TY - GEN
T1 - Nuclei classification using dual view CNNs with multi-crop module in histology images
AU - Li, Xiang
AU - Li, Wei
AU - Zhang, Mengmeng
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Histopathology image diagnostic technique is a quite common requirement; however, cell nuclei classification is still one of key challenge due to complex tissue structure and diversity of nuclear morphology. Cell nuclei categories are often defined by contextual information, including central nucleus and surrounding background. In this paper, we propose a Dual-View Convolutional Neural Networks (DV-CNNs) that captures contextual contents from different views. The DV-CNNs are composed of two independent pathways, one for global region and another for center local region. Noted that each pathway with “multi-crop module” can extract five different feature regions. Common networks do not fully utilize the local information, but the designed cropping module catches information for more complete features. In experiments, two pipelines are complementary to each other in score fusion. To verify the performance in proposed framework, it is evaluated on a colorectal adenocarcinoma image database with more than 20,000 nuclei. Compared with existing methods, our proposed DV-CNNs with multi-crop module demonstrate better performance.
AB - Histopathology image diagnostic technique is a quite common requirement; however, cell nuclei classification is still one of key challenge due to complex tissue structure and diversity of nuclear morphology. Cell nuclei categories are often defined by contextual information, including central nucleus and surrounding background. In this paper, we propose a Dual-View Convolutional Neural Networks (DV-CNNs) that captures contextual contents from different views. The DV-CNNs are composed of two independent pathways, one for global region and another for center local region. Noted that each pathway with “multi-crop module” can extract five different feature regions. Common networks do not fully utilize the local information, but the designed cropping module catches information for more complete features. In experiments, two pipelines are complementary to each other in score fusion. To verify the performance in proposed framework, it is evaluated on a colorectal adenocarcinoma image database with more than 20,000 nuclei. Compared with existing methods, our proposed DV-CNNs with multi-crop module demonstrate better performance.
KW - Cell nuclei classification
KW - Convolutional neural network
KW - Histopathology image analysis
UR - http://www.scopus.com/inward/record.url?scp=85057196224&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-03335-4_20
DO - 10.1007/978-3-030-03335-4_20
M3 - Conference contribution
AN - SCOPUS:85057196224
SN - 9783030033347
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 227
EP - 236
BT - Pattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings
A2 - Liu, Cheng-Lin
A2 - Tan, Tieniu
A2 - Zhou, Jie
A2 - Lai, Jian-Huang
A2 - Chen, Xilin
A2 - Zheng, Nanning
A2 - Zha, Hongbin
PB - Springer Verlag
T2 - 1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018
Y2 - 23 November 2018 through 26 November 2018
ER -