TY - GEN
T1 - S4ND
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
AU - Khosravan, Naji
AU - Bagci, Ulas
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - The most recent lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge and provide better performance, in this paper we propose S4ND, a new deep learning based method for lung nodule detection. Our approach uses a single feed forward pass of a single network for detection. The whole detection pipeline is designed as a single 3D Convolutional Neural Network (CNN) with dense connections, trained in an end-to-end manner. S4ND does not require any further post-processing or user guidance to refine detection results. Experimentally, we compared our network with the current state-of-the-art object detection network (SSD) in computer vision as well as the state-of-the-art published method for lung nodule detection (3D DCNN). We used publicly available 888 CT scans from LUNA challenge dataset and showed that the proposed method outperforms the current literature both in terms of efficiency and accuracy by achieving an average FROC-score of 0.897. We also provide an in-depth analysis of our proposed network to shed light on the unclear paradigms of tiny object detection.
AB - The most recent lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge and provide better performance, in this paper we propose S4ND, a new deep learning based method for lung nodule detection. Our approach uses a single feed forward pass of a single network for detection. The whole detection pipeline is designed as a single 3D Convolutional Neural Network (CNN) with dense connections, trained in an end-to-end manner. S4ND does not require any further post-processing or user guidance to refine detection results. Experimentally, we compared our network with the current state-of-the-art object detection network (SSD) in computer vision as well as the state-of-the-art published method for lung nodule detection (3D DCNN). We used publicly available 888 CT scans from LUNA challenge dataset and showed that the proposed method outperforms the current literature both in terms of efficiency and accuracy by achieving an average FROC-score of 0.897. We also provide an in-depth analysis of our proposed network to shed light on the unclear paradigms of tiny object detection.
KW - Deep learning
KW - Dense CNN
KW - Lung nodule detection
KW - Object detection
KW - Tiny object detection
UR - http://www.scopus.com/inward/record.url?scp=85054092037&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054092037&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00934-2_88
DO - 10.1007/978-3-030-00934-2_88
M3 - Conference contribution
AN - SCOPUS:85054092037
SN - 9783030009335
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 794
EP - 802
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Fichtinger, Gabor
A2 - Davatzikos, Christos
A2 - Alberola-López, Carlos
A2 - Frangi, Alejandro F.
A2 - Schnabel, Julia A.
PB - Springer Verlag
Y2 - 16 September 2018 through 20 September 2018
ER -