Yeseul Jeon

Jeong-Jae Kim, Yeseul Jeon, SuMin Yu et al.

There have been several attempts to use deep learning based on brain fMRI signals to classify cognitive impairment diseases. However, deep learning is a hidden black box model that makes it difficult to interpret the process of classification. To address this issue, we propose a novel analytical framework that interprets the classification result from deep learning processes. We first derive the region of interest (ROI) functional connectivity network (FCN) by embedding functions based on their similar signal patterns. Then, using the self-attention equipped deep learning model, we classify diseases based on their FCN. Finally, in order to interpret the classification results, we employ a latent space item-response interaction network model to identify the significant functions that exhibit distinct connectivity patterns when compared to other diseases. The application of this proposed framework to the four types of cognitive impairment shows that our approach is valid for determining the significant ROI functions.

Hen-Wei Huang, Claas Ehmke, Dawei Wang et al.

Ingestible electronic systems enable non-invasive, in situ sensing within the gastrointestinal (GI) tract, yet clinical translation has been limited by uncontrolled transit, short operational lifetimes, and unreliable wireless communication that prevent continuous monitoring. Here, we present a gastric-resident ingestible robotic platform that achieves week-long operation through integration of a bioinspired, electrically triggered release mechanism with a kirigami-enabled electronic architecture. A kirigami-patterned flexible printed circuit board spans the capsule body and deployable superelastic arms, enabling high-density integration of sensing, power management, and wireless modules within a constrained volume while tolerating large mechanical deformation during gastric residence. Stable retention and on-demand disassembly are achieved using thermally responsive polycaprolactone joints that transition from rigid to compliant states under electrical activation, avoiding dependence on variable chemical triggers. Reliable telemetry in the highly attenuating gastric environment is maintained using a dual-band Bluetooth Low Energy and sub-gigahertz module with RSSI- and throughput-aware adaptive transmission, balancing link robustness and energy consumption. We demonstrate long-term, continuous monitoring of gastric radiation exposure, enabling early detection of dose accumulation and providing a promising in vivo alternative to wearable or handheld dosimeters. Swine studies confirm stable gastric residence, sustained real-time telemetry, and safe gastrointestinal passage following triggered disassembly. This work establishes kirigami-enabled integration as a scalable strategy for long-term gastric-resident robotic systems.

Bowen Lei, Yeseul Jeon, Rajarshi Guhaniyogi et al.

Understanding relationships across multiple imaging modalities is central to neuroimaging research. We introduce the Integrative Variational Autoencoder (InVA), the first hierarchical VAE framework for image-on-image regression in multimodal neuroimaging. Unlike standard VAEs, which are not designed for predictive integration across modalities, InVA models outcome images as functions of both shared and modality-specific features. This flexible, data-driven approach avoids rigid assumptions of classical tensor regression and outperforms conventional VAEs and nonlinear models such as BART. As a key application, InVA accurately predicts costly PET scans from structural MRI, offering an efficient and powerful tool for multimodal neuroimaging.

Yeseul Jeon, Dongjun Chung, Jina Park et al.

Since the emergence of the worldwide pandemic of COVID-19, relevant research has been published at a dazzling pace, which yields an abundant amount of big data in biomedical literature. Due to the high volum of relevant literature, it is practically impossible to follow up the research manually. Topic modeling is a well-known unsupervised learning that aims to reveal latent topics from text data. In this paper, we propose a novel analytical framework for estimating topic interactions and effective visualization to improve topics' relationships. We first estimate topic-word distributions using the biterm topic model and estimate the topics' interaction based on the word distribution using the latent space item response model. We mapped these latent topics onto networks to visualize relationships among the topics. Moreover, in the proposed approach, we developed a score that is helpful in selecting meaningful words that characterize the topic. We figure out how topics are related by looking at how their relationships change. We do this with a "trajectory plot" that is made with different levels of word richness. These findings provide a thoroughly mined and intuitive representation of relationships between topics related to a specific research area. The application of this proposed framework to the PubMed literature demonstrates utility of our approach in understanding of the topic composition related to COVID-19 studies in the stage of its emergence.