Mitosis detection plays an important role in the analysis of cell status and behavior and is therefore widely utilized in many biological research and medical applications. In this article, we propose a deep reinforcement learning-based progressive sequence saliency discovery network (PSSD)for mitosis detection in time-lapse phase contrast microscopy images. By discovering the salient frames when cell state changes in the sequence, PSSD can more effectively model the mitosis process for mitosis detection. We formulate the discovery of salient frames as a Markov Decision Process (MDP)that progressively adjusts the selection positions of salient frames in the sequence, and further leverage deep reinforcement learning to learn the policy in the salient frame discovery process. 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 C2C12-16.

中文翻译：

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基于深度强化学习的渐进序列显着性发现网络用于延时相位对比显微镜图像中的有丝分裂检测

有丝分裂检测在细胞状态和行为分析中起着重要作用，因此被广泛用于许多生物学研究和医学应用中。在本文中，我们提出了一种基于深度强化学习的渐进序列显着性发现网络 (PSSD)，用于在延时相差显微镜图像中检测有丝分裂。通过发现序列中细胞状态变化时的显着帧，PSSD 可以更有效地模拟有丝分裂过程以进行有丝分裂检测。我们将显着帧的发现制定为马尔可夫决策过程（MDP），它逐步调整序列中显着帧的选择位置，并进一步利用深度强化学习来学习显着帧发现过程中的策略。所提出的方法由两部分组成：1）显着性发现模块，通过逐步调整显着帧的选择位置，从输入细胞图像序列中选择显着帧；2）有丝分裂识别模块，它采用一系列显着帧并进行时间信息融合以进行有丝分裂序列分类。由于显着性发现模块的策略网络是在有丝分裂识别模块的指导下训练的，PSSD可以全面探索有利于有丝分裂检测的显着帧。据我们所知，这是对有丝分裂检测问题实施深度强化学习的第一项工作。在实验中，我们在最大的有丝分裂检测数据集 C2C12-16 上评估了所提出的方法。实验结果表明，与 state-of-the-arts 相比，