Image shows representative example of the training results on 2922Q1 data set with significant performance improvement resulting from the applied bias correction.
Intensity of acquired electron microscopy data is subjected to large variability due to the interplay of many different factors, such as microscope and camera settings used for data acquisition, sample thickness, specimen staining protocol and more. In this work, we developed an efficient method for performing intensity inhomogeneity correction on a single set of combined transmission electron microscopy (TEM) images and demonstrated its positive impact on training a neural network on these data.
Oleh Dzyubachyk, Roman I Koning, Aat A Mulder, M. Christina Avramut, Frank GA Faas, Abraham J Koster. Intensity Correction and Standardization for Electron Microscopy Data. Proceedings of Machine Learning Research 1–9, 2021 (https://openreview.net/group?id=MIDL.io/2021/Conference)