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Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale.

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Title: Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale.
Authors: Cai, Jinzheng1 (AUTHOR) caijinzheng883@paii-labs.com, Harrison, Adam P.1 (AUTHOR) adampharrison070@paii-labs.com, Zheng, Youjing2 (AUTHOR) zhengyoujing@vt.edu, Yan, Ke1 (AUTHOR) yanke383@paii-labs.com, Huo, Yuankai3 (AUTHOR) yuankai.huo@vanderbilt.edu, Xiao, Jing4 (AUTHOR) xiaojing661@pingan.com.cn, Yang, Lin5 (AUTHOR) lin.yang@bme.ufl.edu, Lu, Le1 (AUTHOR) le.lu@paii-labs.com
Source: IEEE Transactions on Medical Imaging. Jan2021, Vol. 40 Issue 1, p59-70. 12p.
Subjects: Mines & mineral resources, Machine learning, Computer-assisted image analysis (Medicine), Detectors, Medical imaging systems, Conveyor belts
Abstract: The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address this problem, but labels often incomplete or noisy, e.g., 50% of the lesions in DeepLesion are left unlabeled. Thus, effective label harvesting methods are critical. This is the goal of our work, where we introduce Lesion-Harvester—a powerful system to harvest missing annotations from lesion datasets at high precision. Accepting the need for some degree of expert labor, we use a small fully-labeled image subset to intelligently mine annotations from the remainder. To do this, we chain together a highly sensitive lesion proposal generator (LPG) and a very selective lesion proposal classifier (LPC). Using a new hard negative suppression loss, the resulting harvested and hard-negative proposals are then employed to iteratively finetune our LPG. While our framework is generic, we optimize our performance by proposing a new 3D contextual LPG and by using a global-local multi-view LPC. Experiments on DeepLesion demonstrate that Lesion-Harvester can discover an additional 9,805 lesions at a precision of 90%. We publicly release the harvested lesions, along with a new test set of completely annotated DeepLesion volumes. We also present a pseudo 3D IoU evaluation metric that corresponds much better to the real 3D IoU than current DeepLesion evaluation metrics. To quantify the downstream benefits of Lesion-Harvester we show that augmenting the DeepLesion annotations with our harvested lesions allows state-of-the-art detectors to boost their average precision by 7 to 10%. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address this problem, but labels often incomplete or noisy, e.g., 50% of the lesions in DeepLesion are left unlabeled. Thus, effective label harvesting methods are critical. This is the goal of our work, where we introduce Lesion-Harvester—a powerful system to harvest missing annotations from lesion datasets at high precision. Accepting the need for some degree of expert labor, we use a small fully-labeled image subset to intelligently mine annotations from the remainder. To do this, we chain together a highly sensitive lesion proposal generator (LPG) and a very selective lesion proposal classifier (LPC). Using a new hard negative suppression loss, the resulting harvested and hard-negative proposals are then employed to iteratively finetune our LPG. While our framework is generic, we optimize our performance by proposing a new 3D contextual LPG and by using a global-local multi-view LPC. Experiments on DeepLesion demonstrate that Lesion-Harvester can discover an additional 9,805 lesions at a precision of 90%. We publicly release the harvested lesions, along with a new test set of completely annotated DeepLesion volumes. We also present a pseudo 3D IoU evaluation metric that corresponds much better to the real 3D IoU than current DeepLesion evaluation metrics. To quantify the downstream benefits of Lesion-Harvester we show that augmenting the DeepLesion annotations with our harvested lesions allows state-of-the-art detectors to boost their average precision by 7 to 10%. [ABSTRACT FROM AUTHOR]
ISSN:02780062
DOI:10.1109/TMI.2020.3022034