TY - GEN
T1 - Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks
AU - Gadermayr, M.
AU - Tschuchnig, M.
AU - Stangassinger, L.M.
AU - Kreutzer, C.
AU - Couillard-Despres, S.
AU - Oostingh, G.J.
AU - Hittmair, A.
N1 - Conference code: 265759
Cited By :3
Export Date: 14 December 2023
Correspondence Address: Gadermayr, M.; Department of Information Technology and Systems Management, Austria; email: [email protected]
Funding details: FHS-2019-10-KIAMed
Funding text 1: Acknowledgement. This work was partially funded by the County of Salzburg under grant number FHS-2019-10-KIAMed.
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PY - 2021/9
Y1 - 2021/9
N2 - In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during the intervention to base intra-operative decisions on the outcome of the histology. However, compared to paraffin sections, the quality of frozen sections is typically lower, leading to a higher ratio of miss-classification. In this work, we investigated the effect of the section type on automated decision support approaches for classification of thyroid cancer. This was enabled by a data set consisting of pairs of sections for individual patients. Moreover, we investigated, whether a frozen-to-paraffin translation could help to optimize classification scores. Finally, we propose a specific data augmentation strategy to deal with a small amount of training data and to increase classification accuracy even further. © 2021, Springer Nature Switzerland AG.
AB - In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during the intervention to base intra-operative decisions on the outcome of the histology. However, compared to paraffin sections, the quality of frozen sections is typically lower, leading to a higher ratio of miss-classification. In this work, we investigated the effect of the section type on automated decision support approaches for classification of thyroid cancer. This was enabled by a data set consisting of pairs of sections for individual patients. Moreover, we investigated, whether a frozen-to-paraffin translation could help to optimize classification scores. Finally, we propose a specific data augmentation strategy to deal with a small amount of training data and to increase classification accuracy even further. © 2021, Springer Nature Switzerland AG.
KW - Data augmentation
KW - Frozen sections
KW - Generative adversarial networks
KW - Histology
KW - Thyroid cancer
KW - Whole slide image classification
KW - Classification (of information)
KW - Decision support systems
KW - Diseases
KW - Image classification
KW - Medical computing
KW - Medical imaging
KW - Paraffins
KW - Decision supports
KW - Images classification
KW - Intra-operative
KW - Section types
KW - Surgical interventions
KW - Thyroid cancers
KW - Whole slide images
U2 - 10.1007/978-3-030-87592-3_10
DO - 10.1007/978-3-030-87592-3_10
M3 - Conference contribution
SN - 978-3-030-87591-6
VL - 12965 LNCS
T3 - Lecture Notes in Computer Science
SP - 99
EP - 109
BT - Simulation and Synthesis in Medical Imaging
PB - Springer Nature
T2 - 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021
ER -
SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure