TY - GEN
T1 - Mtmr-net
T2 - 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018
AU - Liu, Lihao
AU - Dou, Qi
AU - Chen, Hao
AU - Olatunji, Iyiola E.
AU - Qin, Jing
AU - Heng, Pheng Ann
N1 - Funding Information:
This project is funded by Hong Kong Innovation and Technology Commission, under ITSP Tier 2 Scheme (Project No. ITS/426/17FP).
Funding Information:
Acknowledgement. This project is funded by Hong Kong Innovation and Technology Commission, under ITSP Tier 2 Scheme (Project No. ITS/426/17FP).
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018/9
Y1 - 2018/9
N2 - Lung cancer is the leading cause of cancer deaths worldwide. Early diagnosis of lung nodules is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score grading. However, this is quite challenging due to the considerable difficulty of nodule heterogeneity modelling and limited discrimination capability on ambiguous cases. To meet these challenges, we propose a Multi-Task deep learning framework with a novel Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. The relatedness between lung nodule classification and attribute score regression is explicitly explored in our multi-task model, which can contribute to the performance gains of both tasks. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. Furthermore, a siamese network with a novel margin ranking loss was elaborately designed to enhance the discrimination capability on ambiguous nodule cases. We validated the efficacy of our MTMR-Net on the public benchmark LIDC-IDRI dataset. Extensive experiments demonstrated that our approach achieved competitive classification performance and more accurate attribute scoring over the state-of-the-arts.
AB - Lung cancer is the leading cause of cancer deaths worldwide. Early diagnosis of lung nodules is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score grading. However, this is quite challenging due to the considerable difficulty of nodule heterogeneity modelling and limited discrimination capability on ambiguous cases. To meet these challenges, we propose a Multi-Task deep learning framework with a novel Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. The relatedness between lung nodule classification and attribute score regression is explicitly explored in our multi-task model, which can contribute to the performance gains of both tasks. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. Furthermore, a siamese network with a novel margin ranking loss was elaborately designed to enhance the discrimination capability on ambiguous nodule cases. We validated the efficacy of our MTMR-Net on the public benchmark LIDC-IDRI dataset. Extensive experiments demonstrated that our approach achieved competitive classification performance and more accurate attribute scoring over the state-of-the-arts.
UR - http://www.scopus.com/inward/record.url?scp=85057240622&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00889-5_9
DO - 10.1007/978-3-030-00889-5_9
M3 - Conference article published in proceeding or book
AN - SCOPUS:85057240622
SN - 9783030008888
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 74
EP - 82
BT - Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018
A2 - Maier-Hein, Lena
A2 - Syeda-Mahmood, Tanveer
A2 - Taylor, Zeike
A2 - Lu, Zhi
A2 - Stoyanov, Danail
A2 - Madabhushi, Anant
A2 - Tavares, João Manuel R.S.
A2 - Nascimento, Jacinto C.
A2 - Moradi, Mehdi
A2 - Martel, Anne
A2 - Papa, Joao Paulo
A2 - Conjeti, Sailesh
A2 - Belagiannis, Vasileios
A2 - Greenspan, Hayit
A2 - Carneiro, Gustavo
A2 - Bradley, Andrew
PB - Springer Verlag
Y2 - 20 September 2018 through 20 September 2018
ER -
