AI-based automated active learning for discovery of hidden dynamic
  processes: A use case in light microscopy
Nils Friederich Angelo Yamachui Sitcheu Oliver Neumann Süheyla Eroğlu-Kayıkçı Roshan Prizak Lennart Hilbert Ralf Mikut
In the biomedical environment, experiments assessing dynamic processes are
primarily performed by a human acquisition supervisor. Contemporary
implementations of such experiments frequently aim to acquire a maximum number
of relevant events from sometimes several hundred parallel, non-synchronous
processes. Since in some high-throughput experiments, only one or a few
instances of a given process can be observed simultaneously, a strategy for
planning and executing an efficient acquisition paradigm is essential. To
address this problem, we present two new methods in this paper. The first
method, Encoded Dynamic Process (EDP), is Artificial Intelligence (AI)-based
and represents dynamic processes so as to allow prediction of pseudo-time
values from single still images. Second, with Experiment Automation Pipeline
for Dynamic Processes (EAPDP), we present a Machine Learning Operations
(MLOps)-based pipeline that uses the extracted knowledge from EDP to
efficiently schedule acquisition in biomedical experiments for dynamic
processes in practice. In a first experiment, we show that the pre-trained
State-Of-The- Art (SOTA) object segmentation method Contour Proposal Networks
(CPN) works reliably as a module of EAPDP to extract the relevant object for
EDP from the acquired three-dimensional image stack.