Background: Nine E-learning modules (ELMs) were developed in our program using Articulate software. This study assessed our cytotechnology (CT) students' perceptions on the content of the ELMs, and the perceived influence of the ELMs on students' performance during clinical rotations. Subjects and Methods: All CT students watched nine ELMs before the related classroom lecture and group discussion. Following that, students completed nine preclinical rotation surveys. After their clinical rotations, students completed nine postclinical rotation surveys. Results: Statements on the content of the ELMs regarding the quality of the video and audio, duration, navigation, and the materials presented, received positive responses from the majority of the students. While there were a few disagreements and neutral responses, most of the students responded positively saying that the ELMs better prepared them for their role, as well as helped them to better perform their roles during the clinical rotation. The majority of the students recommended developing more EMLs for cytology courses in the future Conclusions: This study has given hope that the ELMs have potential to enhance our online curriculum and benefit students, within the United States and internationally, who have no easy access to cytology clinical laboratories for hands-on training.

The advent of the digital pathology has introduced new avenues of diagnostic medicine. Among them, crowdsourcing has attracted researchers' attention in the recent years, allowing them to engage thousands of untrained individuals in research and diagnosis. While there exist several articles in this regard, prior works have not collectively documented them. We, therefore, aim to review the applications of crowdsourcing in human pathology in a semi-systematic manner. We first, introduce a novel method to do a systematic search of the literature. Utilizing this method, we, then, collect hundreds of articles and screen them against a predefined set of criteria. Furthermore, we crowdsource part of the screening process, to examine another potential application of crowdsourcing. Finally, we review the selected articles and characterize the prior uses of crowdsourcing in pathology.

Context: Medical diagnosis and clinical decisions rely heavily on the histopathological evaluation of tissue samples, especially in oncology. Historically, classical histopathology has been the gold standard for tissue evaluation and assessment by pathologists. The most widely and commonly used dyes in histopathology are hematoxylin and eosin (H&E) as most malignancies diagnosis is largely based on this protocol. H&E staining has been used for more than a century to identify tissue characteristics and structures morphologies that are needed for tumor diagnosis. In many cases, as tissue is scarce in clinical studies, fluorescence imaging is necessary to allow staining of the same specimen with multiple biomarkers simultaneously. Since fluorescence imaging is a relatively new technology in the pathology landscape, histopathologists are not used to or trained in annotating or interpreting these images. Aims, Settings and Design: To allow pathologists to annotate these images without the need for additional training, we designed an algorithm for the conversion of fluorescence images to brightfield H&E images. Subjects and Methods: In this algorithm, we use fluorescent nuclei staining to reproduce the hematoxylin information and natural tissue autofluorescence to reproduce the eosin information avoiding the necessity to specifically stain the proteins or intracellular structures with an additional fluorescence stain. Statistical Analysis Used: Our method is based on optimizing a transform function from fluorescence to H&E images using least mean square optimization. Results: It results in high quality virtual H&E digital images that can easily and efficiently be analyzed by pathologists. We validated our results with pathologists by making them annotate tumor in real and virtual H&E whole slide images and we obtained promising results. Conclusions: Hence, we provide a solution that enables pathologists to assess tissue and annotate specific structures based on multiplexed fluorescence images.