Deep learning based methods have demonstrated outstanding capabilities in quantifying nuclei and cells in microscopy images. However, differences among various stain modalities would affect the performance of nuclei detection. How to fully utilize limited annotations for nuclei detection in other pathological staining images without annotations has become a significant challenge. This paper proposes an end-to-end unsupervised multi-level semantic consistent generative adversarial network (MSC-GAN) for nuclei detection across different pathological staining modalities. Specifically, we address nuclei detection on the unlabeled target domain data by first transforming the stain modality of the source domain into the target domain, and then utilizing the source domain annotations to train the nuclei detector network. A hierarchical semantic consistency loss including feature-level consistency and mask-level consistency is introduced to offer supplementary supervision to enhance the accuracy of generative adversarial learning. We further design an augmentation module to prevent the discriminator from overfitting. The experimental results on four microscopy image datasets demonstrate that MSC-GAN outperforms state-of-the-art methods in the nuclei detection tasks, achieving superior F1 scores.

Field-programmable gate arrays (FPGAs) have recently evolved as a valuable component of the heterogeneous computing. The register transfer level (RTL) design flows demand the designers to be experienced in hardware, resulting in a possible failure of time-to-market. High-level synthesis (HLS) permits designers to work at a higher level of abstraction through synthesizing high-level language programs to RTL descriptions. This provides a promising approach to solve these problems. However, the performance of HLS tools still has limitations. For example, designers remain exposed to various aspects of hardware design, development cycles are still time consuming, and the quality of results (QoR) of HLS tools is far behind that of RTL flows. In this paper, we survey the literature published since 2014 focusing on the performance optimization of HLS tools. Compared with previous work, we extend the scope of the performance of HLS tools, and present a set of three-level evaluation criteria, covering from ease of use of the HLS tools to promotion on specific metrics of QoR. We also propose performance evaluation equations for describing the relation between the performance optimization and the QoR. We find that it needs more efforts on the ease of use for efficient HLS tools. We suggest that it is better to draw an analogy between the HLS development process and the embedded system design process, and to provide more elastic HLS methodology which integrates FPGAs virtual machines.