arXiv subjects

Hyun Jung Koo

Publications and source records attributed to Hyun Jung Koo.

2 recordsLinked to original sources

arXiv2026

Constrained convex clustering for interpretable spatial domain detection in spot-based spatial transcriptomics

Popular technologies for generating spatially resolved transcriptomic data measure gene expression at the resolution of a "spot", i.e., a small tissue region 55 microns in diameter. Each spot can contain many cells of different types. In typical analyses, researchers are interested in using these data to identify and profile discrete spatial domains in tissue. In this paper, we propose a new method, DUET, which simultaneously identifies discrete spatial domains and estimates each spot's expected cell-type proportion. This allows the identified spatial domains to be characterized in terms of the underlying expected cell-type proportions, which affords interpretability and biological insight. DUET utilizes a constrained version of model-based convex clustering, and as such, can accommodate Poisson, negative binomial, normal, and other types of expression data. Moreover, our convex clustering-type criterion allows for both the number of clusters and degree of spatial smoothness to be controlled by a single tuning parameter, which can be chosen in a data-driven fashion. Through simulation studies and a real data application, we show that DUET can achieve better clustering and deconvolution performance than some existing methods.

stat.AP↗

arXiv2018

Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography

In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. To address this problem, here we propose a novel semi-supervised learning technique that can remove the noises of the CT images obtained in the low-dose phases by learning from the CT images in the routine dose phases. Although a supervised learning approach is not possible due to the differences in the underlying heart structure in two phases, the images in the two phases are closely related so that we propose a cycle-consistent adversarial denoising network to learn the non-degenerate mapping between the low and high dose cardiac phases. Experimental results showed that the proposed method effectively reduces the noise in the low-dose CT image while the preserving detailed texture and edge information. Moreover, thanks to the cyclic consistency and identity loss, the proposed network does not create any artificial features that are not present in the input images. Visual grading and quality evaluation also confirm that the proposed method provides significant improvement in diagnostic quality.

cs.CV↗