Show simple item record

dc.contributor.authorDong, Nanqing
dc.contributor.authorKampffmeyer, Michael C.
dc.contributor.authorLiang, Xiaodan
dc.contributor.authorWang, Zeya
dc.contributor.authorDai, Wei
dc.contributor.authorXing, Eric P.
dc.date.accessioned2019-02-20T14:39:46Z
dc.date.available2019-02-20T14:39:46Z
dc.date.issued2018-09-20
dc.description.abstractConvolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (WSIs) is still underexplored. WSIs are large histopathological images with extremely high resolution. Constrained by the hardware and field of view, using high-magnification patches can slow down the inference process and using low-magnification patches can cause the loss of information. In this paper, we aim to achieve two seemingly conflicting goals for breast cancer segmentation: accurate and fast prediction. We propose a simple yet efficient framework Reinforced Auto-Zoom Net (RAZN) to tackle this task. Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest. Because the zoom-in action is selective, RAZN is robust to unbalanced and noisy ground truth labels and can efficiently reduce overfitting. We evaluate our method on a public breast cancer dataset. RAZN outperforms both single-scale and multi-scale baseline approaches, achieving better accuracy at low inference cost.en_US
dc.descriptionAccepted manuscript version. Published version available at <a href=https://doi.org/10.1007/978-3-030-00889-5_36> https://doi.org/10.1007/978-3-030-00889-5_36</a>.en_US
dc.identifier.citationDong, N., Kampffmeyer, M., Liang, X., Wang, Z., Dai, W. & Xing, E.P. (2018). Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-Slide Images. <i>Lecture Notes in Computer Science</i>. https://doi.org/10.1007/978-3-030-00889-5_36en_US
dc.identifier.cristinIDFRIDAID 1655978
dc.identifier.doi10.1007/978-3-030-00889-5_36
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/10037/14733
dc.language.isoengen_US
dc.publisherSpringer Verlag (Germany)en_US
dc.relation.journalLecture Notes in Computer Science
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.subjectBreast canceren_US
dc.subjectDeep reinforcement learningen_US
dc.subjectMedical image segmentationen_US
dc.subjectWhole-slide imagesen_US
dc.titleReinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-Slide Imagesen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


File(s) in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record