Iterative learning to make the most of unlabeled and quickly obtained labeled data in histology

Laxmi Gupta, Barbara Mara Klinkhammer, Peter Boor, Dorit Merhof, Michael Gadermayr
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:215-224, 2019.

Abstract

Due to the increasing availability of digital whole slide scanners, the importance of image analysis in the field of digital pathology increased significantly. A major challenge and an equally big opportunity for analyses in this field is given by the wide range of tasks and different histological stains. Although sufficient image data is often available for training, the requirement for corresponding expert annotations inhibits clinical deployment. Thus, there is an urgent need for methods which can be effectively trained with or adapted to a small amount of labeled training data. Here, we propose a method to find an optimum trade-off between (low) annotation effort and (high) segmentation accuracy. For this purpose, we propose an approach based on a weakly supervised and an unsupervised learning stage relying on few roughly labeled samples and many unlabeled samples. Although the idea of weakly annotated data is not new, we firstly investigate the applicability to digital pathology in a state-of-the-art machine learning setting.

Cite this Paper


BibTeX
@InProceedings{pmlr-v102-gupta19a, title = {Iterative learning to make the most of unlabeled and quickly obtained labeled data in histology}, author = {Gupta, Laxmi and {Mara Klinkhammer}, Barbara and Boor, Peter and Merhof, Dorit and Gadermayr, Michael}, booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning}, pages = {215--224}, year = {2019}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, volume = {102}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v102/gupta19a/gupta19a.pdf}, url = { http://proceedings.mlr.press/v102/gupta19a.html }, abstract = {Due to the increasing availability of digital whole slide scanners, the importance of image analysis in the field of digital pathology increased significantly. A major challenge and an equally big opportunity for analyses in this field is given by the wide range of tasks and different histological stains. Although sufficient image data is often available for training, the requirement for corresponding expert annotations inhibits clinical deployment. Thus, there is an urgent need for methods which can be effectively trained with or adapted to a small amount of labeled training data. Here, we propose a method to find an optimum trade-off between (low) annotation effort and (high) segmentation accuracy. For this purpose, we propose an approach based on a weakly supervised and an unsupervised learning stage relying on few roughly labeled samples and many unlabeled samples. Although the idea of weakly annotated data is not new, we firstly investigate the applicability to digital pathology in a state-of-the-art machine learning setting.} }
Endnote
%0 Conference Paper %T Iterative learning to make the most of unlabeled and quickly obtained labeled data in histology %A Laxmi Gupta %A Barbara Mara Klinkhammer %A Peter Boor %A Dorit Merhof %A Michael Gadermayr %B Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2019 %E M. Jorge Cardoso %E Aasa Feragen %E Ben Glocker %E Ender Konukoglu %E Ipek Oguz %E Gozde Unal %E Tom Vercauteren %F pmlr-v102-gupta19a %I PMLR %P 215--224 %U http://proceedings.mlr.press/v102/gupta19a.html %V 102 %X Due to the increasing availability of digital whole slide scanners, the importance of image analysis in the field of digital pathology increased significantly. A major challenge and an equally big opportunity for analyses in this field is given by the wide range of tasks and different histological stains. Although sufficient image data is often available for training, the requirement for corresponding expert annotations inhibits clinical deployment. Thus, there is an urgent need for methods which can be effectively trained with or adapted to a small amount of labeled training data. Here, we propose a method to find an optimum trade-off between (low) annotation effort and (high) segmentation accuracy. For this purpose, we propose an approach based on a weakly supervised and an unsupervised learning stage relying on few roughly labeled samples and many unlabeled samples. Although the idea of weakly annotated data is not new, we firstly investigate the applicability to digital pathology in a state-of-the-art machine learning setting.
APA
Gupta, L., Mara Klinkhammer, B., Boor, P., Merhof, D. & Gadermayr, M.. (2019). Iterative learning to make the most of unlabeled and quickly obtained labeled data in histology. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 102:215-224 Available from http://proceedings.mlr.press/v102/gupta19a.html .

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