TY - GEN
T1 - Detecting bone lesions in multiple myeloma patients using transfer learning
AU - Perkonigg, Matthias
AU - Hofmanninger, Johannes
AU - Menze, Björn
AU - Weber, Marc André
AU - Langs, Georg
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - The detection of bone lesions is important for the diagnosis and staging of multiple myeloma patients. The scarce availability of annotated data renders training of automated detectors challenging. Here, we present a transfer learning approach using convolutional neural networks to detect bone lesions in computed tomography imaging data. We compare different learning approaches, and demonstrate that pretraining a convolutional neural network on natural images improves detection accuracy. Also, we describe a patch extraction strategy which encodes different information into each input channel of the networks. We train and evaluate our approach on a dataset with 660 annotated bone lesions, and show how the resulting marker map high-lights lesions in computed tomography imaging data.
AB - The detection of bone lesions is important for the diagnosis and staging of multiple myeloma patients. The scarce availability of annotated data renders training of automated detectors challenging. Here, we present a transfer learning approach using convolutional neural networks to detect bone lesions in computed tomography imaging data. We compare different learning approaches, and demonstrate that pretraining a convolutional neural network on natural images improves detection accuracy. Also, we describe a patch extraction strategy which encodes different information into each input channel of the networks. We train and evaluate our approach on a dataset with 660 annotated bone lesions, and show how the resulting marker map high-lights lesions in computed tomography imaging data.
UR - http://www.scopus.com/inward/record.url?scp=85054790982&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00807-9_3
DO - 10.1007/978-3-030-00807-9_3
M3 - Conference contribution
AN - SCOPUS:85054790982
SN - 9783030008062
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 22
EP - 30
BT - Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis - First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018 Held in Conjunction with MICCAI 2018, Proceedings
A2 - Melbourne, Andrew
A2 - Aughwane, Rosalind
A2 - Robinson, Emma
A2 - Licandro, Roxane
A2 - Gau, Melanie
A2 - Kampel, Martin
A2 - DiFranco, Matthew
A2 - Rota, Paolo
A2 - Licandro, Roxane
A2 - Moeskops, Pim
A2 - Schwartz, Ernst
A2 - Makropoulos, Antonios
PB - Springer Verlag
T2 - 1st International Workshop on Data Driven Treatment Response Assessment, DATRA 2018 and 3rd International Workshop on Preterm, Perinatal, and Paediatric Image Analysis, PIPPI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -