TY - GEN
T1 - A deep residual inception network for HEp-2 cell classification
AU - Li, Yuexiang
AU - Shen, Linlin
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Indirect-immunofluorescence (IIF) of Human Epithelial-2 (HEp-2) cells is a commonly-used method for the diagnosis of autoimmune diseases. Traditional approach relies on specialists to observe HEp-2 slides via the fluorescence microscope, which suffers from a number of shortcomings like being subjective and labor intensive. In this paper, we proposed a hybrid deep learning network combining the latest high-performance network architectures, i.e. ResNet and Inception, to automatically classify HEp-2 cell images. The proposed Deep Residual Inception (DRI) net replaces the plain convolutional layers in Inception with residual modules for better network optimization and fuses the features extracted from shallow, medium and deep layers for performance improvement. The proposed model is evaluated on publicly available I3A (Indirect Immunofluorescence Image Analysis) dataset. The experiment results demonstrate that our proposed DRI remarkably outperforms the benchmarking approaches.
AB - Indirect-immunofluorescence (IIF) of Human Epithelial-2 (HEp-2) cells is a commonly-used method for the diagnosis of autoimmune diseases. Traditional approach relies on specialists to observe HEp-2 slides via the fluorescence microscope, which suffers from a number of shortcomings like being subjective and labor intensive. In this paper, we proposed a hybrid deep learning network combining the latest high-performance network architectures, i.e. ResNet and Inception, to automatically classify HEp-2 cell images. The proposed Deep Residual Inception (DRI) net replaces the plain convolutional layers in Inception with residual modules for better network optimization and fuses the features extracted from shallow, medium and deep layers for performance improvement. The proposed model is evaluated on publicly available I3A (Indirect Immunofluorescence Image Analysis) dataset. The experiment results demonstrate that our proposed DRI remarkably outperforms the benchmarking approaches.
KW - Deep learning network
KW - HEp-2 cells
KW - Image classification
UR - http://www.scopus.com/inward/record.url?scp=85029788200&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67558-9_2
DO - 10.1007/978-3-319-67558-9_2
M3 - Conference contribution
AN - SCOPUS:85029788200
SN - 9783319675572
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 12
EP - 20
BT - Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings
A2 - Arbel, Tal
A2 - Cardoso, M. Jorge
PB - Springer Verlag
T2 - 3rd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017 and 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 14 September 2017 through 14 September 2017
ER -
