Deep Reinforcement Learning Based Progressive Sequence Saliency Discovery Network for Mitosis Detection In Time-Lapse Phase-Contrast Microscopy Images.

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ID: 110430
2020
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Ranked #5 of 27 articles by views in ieee/acm transactions on computational biology and bioinformatics

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Abstract
In this paper, we propose a deep reinforcement learning-based progressive sequence saliency discovery network (PSSD) for mitosis detection in time-lapse phase contrast microscopy images. The proposed method consists of two parts: 1) the saliency discovery module that selects the salient frames from the input cell image sequence by progressively adjusting the selection positions of salient frames; 2) the mitosis identification module that takes a sequence of salient frames and performs temporal information fusion for mitotic sequence classification. Since the policy network of the saliency discovery module is trained under the guidance of the mitosis identification module, PSSD can comprehensively explore the salient frames that are beneficial for mitosis detection. To our knowledge, this is the first work to implement deep reinforcement learning to the mitosis detection problem. In the experiment, we evaluate the proposed method on the largest mitosis detection dataset, C2C12-16. Experiment results show that compared with the state of the arts, the proposed method can achieve significant improvement for both mitosis identification and temporal localization on C2C12.
Reference Key
su2020deepieeeacm Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Su, Yuting;Lu, Yao;Chen, Mei;Liu, An-An;
Journal ieee/acm transactions on computational biology and bioinformatics
Year 2020
DOI
10.1109/TCBB.2020.3019042
URL
Keywords
survival analysis explainable ai interpretable model censored data kolmogorov–smirnov bounds the cox model

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