The gold standard in histology is to use chemical stains or genetic modified tissue, where some internal structures emit a fluorescent signal. These methods require trained staff and several hours or days of preparation. Virtual staining employs trained neural networks to take over the staining process. Based on an unlabeled microscopic images the network can predict the corresponding fluorescent image for DAPI and Phalloidin488 staining, enabling studies on cell nuclei and the cytoskeleton.
Neural networks usually need a huge amount of training data, so the possibilities of transfer learning for a reduction of the dataset size were investigated. In addition, we also present first studies the interpretability of the trained network to find ideal image acquisition techniques and optimize the training.
Chromatic aberrations can significantly diminish image quality when employing multiple wavelengths for imaging with a single optical system due to dispersion in both optical system and samples. To tackle this problem, we propose a novel approach using an adaptive achromatic lens, which is controlled by a trained Reinforcement Learning agent as part of a machine learning method. Notably, our method corrects chromatic aberrations prior to the imaging process, distinguishing it from conventional software-based post-processing approaches.
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