Flexible and latent structured output learning: Application to histology
Carneiro G, Peng T, Bayer C, Navab N (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9352
Pages Range: 220-228
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783319248875
DOI: 10.1007/978-3-319-24888-2_27
Abstract
Malignant tumors that contain a high proportion of regions deprived of adequate oxygen supply (hypoxia) in areas supplied by a micro vessel (i.e., a microcirculatory supply unit - MCSU) have been shown to present resistance to common cancer treatments. Given the importance of the estimation of this proportion for improving the clinical prognosis of such treatments, a manual annotation has been proposed, which uses two image modalities of the same histological specimen and produces the number and proportion of MCSUs classified as normoxia (normal oxygenation level), chronic hypoxia (limited diffusion), and acute hypoxia (transient disruptions in perfusion), but this manual annotation requires an expertise that is generally not available in clinical settings. Therefore, in this paper, we propose a new methodology that automates this annotation. The major challenge is that the training set comprises weakly labeled samples that only contains the number of MCSU types per sample, which means that we do not have the underlying structure of MCSU locations and classifications. Hence, we formulate this problem as a latent structured output learning that minimizes a high order loss function based on the number of MCSU types, where the underlying MCSU structure is flexible in terms of number of nodes and connections. Using a database of 89 pairs of weakly annotated images (from eight tumors), we show that our methodology produces highly correlated number and proportion of MCSU types compared to the manual annotations.
Involved external institutions
Australia (AU)
Germany (DE)How to cite
APA:
Carneiro, G., Peng, T., Bayer, C., & Navab, N. (2015). Flexible and latent structured output learning: Application to histology. In Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 220-228). Munich, DEU: Springer Verlag.
MLA:
Carneiro, Gustavo, et al. "Flexible and latent structured output learning: Application to histology." Proceedings of the 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015, Munich, DEU Ed. Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang, Springer Verlag, 2015. 220-228.
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