Two-dimensional observation of biological samples at hundreds of nanometers resolution or even below is of high interest for many sensitive medical applications. Recent advances have been obtained over the last ten years with computational imaging. Among them, Fourier Ptychographic Microscopy is of particular interest because of its important super-resolution factor. In complement to traditional intensity images, phase images are also produced. A large set of $\mathcal{N}$ raw images (with typically $\mathcal{N}$ = 225) is, however, required because of the reconstruction process that is involved. In this paper, we address the problem of FPM image reconstruction using a few raw images only (here, $\mathcal{N}$ = 37) as is highly desirable to increase microscope throughput. In contrast to previous approaches, we develop an algorithmic approach based on a physics-informed optimization deep neural network and statistical reconstruction learning. We demonstrate its efficiency with the help of simulations. The forward microscope image formation model is explicitly introduced in the deep neural network model to optimize its weights starting from an initialization that is based on statistical learning. The simulation results that are presented demonstrate the conceptual benefits of the approach. We show that high-quality images are effectively reconstructed without any appreciable resolution degradation. The learning step is also shown to be mandatory.

Digital pathology based on a whole slide imaging system is about to permit a major breakthrough in automated diagnosis for rapid and highly sensitive disease detection. High-resolution FPM (Fourier ptychographic microscopy) slide scanners delivering rich information on biological samples are becoming available. They allow new effective data exploitation for efficient automated diagnosis. However, when the sample thickness becomes comparable to or greater than the microscope depth of field, we report an observation of undesirable contrast change of sub-cellular compartments in phase images around the optimal focal plane, reducing their usability. In this article, a bi-modal U-Net artificial neural network (i.e., a two channels U-Net fed with intensity and phase images) is trained to reinforce specifically targeted sub-cellular compartments contrast for both intensity and phase images. The procedure used to construct a reference database is detailed. It is obtained by exploiting the FPM reconstruction algorithm to explore images around the optimal focal plane with virtual Z-stacking calculations and selecting those with adequate contrast and focus. By construction and once trained, the U-Net is able to simultaneously reinforce targeted cell compartment visibility and compensate for any focus imprecision. It is efficient over a large field of view at high resolution. The interest of the approach is illustrated considering the use-case of Plasmodium falciparum detection in blood smear where improvement in the detection sensitivity is demonstrated without degradation of the specificity. Post-reconstruction FPM image processing with such U-Net and its training procedure is general and applicable to demanding biological screening applications.

We propose a very fast heterodyne technique to recover the amplitude and phase of short optical pulses generated e.g. by a mode-locked laser. A linearly swept frequency source is used to scan the entire optical spectrum of the mode-locked laser in a single continuous sweep. The beat signal is recorded on a fast oscilloscope and then digitally processed allowing the simultaneous recovery of the amplitude and the phase. This measurement is extremely fast and requires no prior spectral information about the signal under test.

In this work, we propose a novel GAN-based architecture, termed Physics-Inspired GAN (PI-GAN), to generate synthetic bimodal data comprising both intensity and phase images as produced through Fourier Ptychographic Microscopy (FPM). By explicitly incorporating the forward model of image formation into the GAN architecture, our approach ensures that the physical relationship between the intensity and phase modalities is preserved throughout the training and generation processes, therefore solving the mode collapse problem encountered in classical GANs. Our approach is evaluated for the classification of the five major types of white blood cells (WBCs) in peripheral blood smears, a domain where severe class imbalance is a major challenge. In particular, basophils represent less than 1% of circulating WBCs, making it difficult to train robust classifiers without synthetic augmentation. To overcome the scarcity of basophil data, we proposed a two-step fine-tuning strategy: first training the PI-GAN to generate neutrophils (a more abundant but morphologically similar class), and then adapting the model to produce basophils. Our results show that the addition of synthetic basophil images allows a great improvement (5% in precision) in the ability to correctly classify basophils. Our approach offers great potential for future hybrid models that combine physics-based priors with the flexibility of deep generative networks.

The diagnosis of many diseases relies, at least on first intention, on an analysis of blood smears acquired with a microscope. However, image quality is often insufficient for the automation of such processing. A promising improvement concerns the acquisition of enriched information on samples. In particular, Quantitative Phase Imaging (QPI) techniques, which allow the digitization of the phase in complement to the intensity, are attracting growing interest. Such imaging allows the exploration of transparent objects not visible in the intensity image using the phase image only. Another direction proposes using stained images to reveal some characteristics of the cells in the intensity image; in this case, the phase information is not exploited. In this paper, we question the interest of using the bi-modal information brought by intensity and phase in a QPI acquisition when the samples are stained. We consider the problem of detecting parasitized red blood cells for diagnosing malaria from stained blood smears using a Deep Neural Network (DNN). Fourier Ptychographic Microscopy (FPM) is used as the computational microscopy framework to produce QPI images. We show that the bi-modal information enhances the detection performance by $4\%$ compared to the intensity image only when the convolution in the DNN is implemented through a complex-based formalism. This proves that the DNN can benefit from the bi-modal enhanced information. We conjecture that these results should extend to other applications processed through QPI acquisition.

In this paper, we propose and demonstrate an original exploitation of relaxation oscillations of erbium-doped-fiber laser ring resonators based on a general model for Er cross-sectional characterization. Its main advantage as compared to other existing exploitations relies on the fact that only weak approximations relative to spatial light distribution in the laser are imposed. It is therefore applicable to various resonator configurations, i.e., independent of the active-media length and losses encountered during the single-pass loop propagation. An experimental demonstration is finally presented by comparing the extracted erbium parameters (intrinsic saturation power and absorption) to those obtained with other methods.