https://www.selleckchem.com/products/dihexa.html We propose a new framework for super-resolution structured illumination microscopy (SR-SIM) based on compressed sensing (CS). Our framework addresses several key problems in SIM, including long readout time and photobleaching. CS has the potential to eliminate these problems because it allows the reduction of the number of measurements, can record an image faster, and excites fluorochromes with less excitation light. Key contribution of our proposed method is that sampling and down-modulation of an object scene are simultaneously performed. The impact of our contribution is demonstrated by simulation-based experiments involving computer-generated super-resolution microscopy images, considering reductions in both data quality and quantity.Skin cancers are the most common cancers with an increased incidence, and a valid, early diagnosis may significantly reduce its morbidity and mortality. Reflectance confocal microscopy (RCM) is a relatively new, non-invasive imaging technique that allows screening lesions at a cellular resolution. However, one of the main disadvantages of the RCM is frequently occurring artifacts which makes the diagnostic process more time consuming and hard to automate using e.g. end-to-end deep learning approach. A tool to automatically determine the RCM mosaic quality could be beneficial for both the lesion classification and informing the user (dermatologist) about its quality in real-time, during the examination procedure. In this work, we propose an attention-based deep network to automatically determine if a given RCM mosaic has an acceptable quality. We achieved accuracy above 87% on the test set which may considerably improve further classification results and the RCM-based examination.We present a new LSTM (P-LSTM Progressive LSTM) network, aiming to predict morphology and states of cell colonies from time-lapse microscopy images. Apparent short-term changes occur in some types of time-laps