## 2 Literature
### Event Detection and Classification  
Even though segmentation remains the core of the subsequent imaging tasks, automated analysis of microscopy image sequences often bypasses the segmentation task. Usually, the detection of cell events under investigation is performed directly from heuristically generated ROIs. Detecting changes in cellular behavior plays a central role in different studies, where the focus is on identifying the changes in cellular growth, mitosis, and death. Such changes may be related to cell shape, division, and movement. They cannot be detected in a single image but require the analysis of video or time-lapse sequences. The difficulty primarily relies on the wide spatial-temporal variability of such phenomena, which requires suitable methods to handle time-varying phenomena. Nevertheless, the infinite spectrum of possible events faces an inherent shortage of labeled data. Automatic and robust approaches to detecting the time and location of cell events from image sequences often make use of the classification task. In microscopy, classification refers to identifying and distinguishing different cell types or states. Classification between other cells, types of tumors (benign or malignant), types of cell states (mitosis detection, alive-dead classification), and types of CIC (Cell-In-Cell) structures [47] are some typical applications. Some recent AI approaches for event cell detection are here presented. [PERSON] et al. [48] and [PERSON] and [PERSON] [49] propose a convolutional long short-term memory (CNN-LSTM) network and a Two stream Bidirectional CNN-LSTM network, respectively, on sequences of single-cell image patches and utilize both spatial and temporal information to detect mitosis events. They report an average precision of 0.96 and 0.98, respectively. However, these models need a large amount of manually annotated data to train on, and both papers also report a sharp decrease in accuracy when testing the model on other cell datasets. A CNN-LSTM model that learns spatial and temporal locations of the cells from a detection map in a semi-supervised manner is proposed by [PERSON] et al. [50] for the detection of mitosis in PhC videos. The method needs only 1050 annotated frames to achieve an F1 score of 0.544-0.822, depending on the video. However, it also shows a decrease in performance with the increase in the input sequence length, which is not ideal for practical situations where time-lapse experiment's video sequences may contain thousands of frames. The method also will only be able to detect a single event at a time, such as mitosis, whereas these events can randomly occur in multiple places in a single frame. [PERSON] and [PERSON] [51] propose a method for multiple mitosis event detection and localization by estimating a spatial-temporal likelihood map using the 3D CNN architecture V-Net [52]. In the likelihood map, a mitosis position is represented as an intensity peak with a Gaussian distribution, in which multiple mitoses are represented as multiple peaks. The method has an average precision of 0.862 on a private dataset. While the method does take into account the spatial and temporal information, it is only limited to detecting mitosis events and not any other events that may be associated with mitosis. In order to identify other events as well, multiple models based on this method would be needed. Furthermore, the use of this method for other datasets or cell lines requires the generation of laborious manual annotations in the form of Gaussian distributed likelihood maps. [PERSON] et al. [53] present a deep reinforcement learning-based progressive sequence saliency discovery network (PSSD) for mitosis detection in time-lapse PhC images. The discovery of these salient frames is formulated as a Markov Decision Process that progressively adjusts the selection positions of salient frames in the sequence. Then, the pipeline leverages deep reinforcement learning to learn the policy in the salient frame discovery process.