ChromoEnhancer: An Artificial-Intelligence-Based Tool to Enhance Neoplastic Karyograms as an Aid for Effective Analysis.

Cytogenetics laboratory tests are among the most important procedures for the diagnosis of genetic diseases, especially in the area of hematological malignancies. Manual chromosomal karyotyping methods are time consuming and labor intensive and, hence, expensive. Therefore, to alleviate the process of analysis, several attempts have been made to enhance karyograms. The current chromosomal image enhancement is based on classical image processing. This approach has its limitations, one of which is that it has a mandatory application to all chromosomes, where customized application to each chromosome is ideal. Moreover, each chromosome needs a different level of enhancement, depending on whether a given area is from the chromosome itself or it is just an artifact from staining. The analysis of poor-quality karyograms, which is a difficulty faced often in preparations from cancer samples, is time consuming and might result in missing the abnormality or difficulty in reporting the exact breakpoint within the chromosome. We developed ChromoEnhancer, a novel artificial-intelligence-based method to enhance neoplastic karyogram images. The method is based on Generative Adversarial Networks (GANs) with a data-centric approach. GANs are known for the conversion of one image domain to another. We used GANs to convert poor-quality karyograms into good-quality images. Our method of karyogram enhancement led to robust routine cytogenetic analysis and, therefore, to accurate detection of cryptic chromosomal abnormalities. To evaluate ChromoEnahancer, we randomly assigned a subset of the enhanced images and their corresponding original (unenhanced) images to two independent cytogeneticists to measure the karyogram quality and the elapsed time to complete the analysis, using four rating criteria, each scaled from 1 to 5. Furthermore, we compared the enhanced images with our method to the original ones, using quantitative measures (PSNR and SSIM metrics).

A Literature Based Discovery Visualization System with Hierarchical Clustering and Linking Set Associations.

Literature Based discovery (LBD) seeks to find information implicit in text, but never explicitly stated. In this work, we develop a method of visually summarizing LBD output in an automatically generated tree structure. This structure promotes a comprehensive understanding of LBD output as a whole, and encourages the user to explore branches of the hierarchy they find most interesting or surprising. This novel visualization system requires the development and integration of automatic functional group discovery, set associations, and linking set associations. Specifically, we perform hierarchical clustering on the potential discoveries generated by an LBD system to create a tree of potential hypotheses. We weight the tree by developing set association measures, and extending them to linking set association measures. This weighted tree is displayed in an interactive visual environment, and validated by replicating the historic Raynaud's Disease - fish oil discovery.