arXiv subjects

Kyungwon Kim

Publications and source records attributed to Kyungwon Kim.

9 recordsLinked to original sources

arXiv2026

CRUX: Clinical-Text Anchoring for Cross-Institutional CT-Pathology Fusion in Non-Small Cell Lung Cancer Survival Prediction

Accurate prognosis in non-small cell lung cancer (NSCLC) benefits from integrating computed tomography (CT) and histopathological whole-slide images (WSIs), which capture complementary macroscopic and microscopic disease characteristics. Fusing these modalities is difficult: their features come from different pretrained foundation-model encoders and lack spatial correspondence, and both are sensitive to institution-specific acquisition and staining, causing multimodal models to overfit site-specific artifacts and generalize poorly. We present CRUX (CRoss-modal Unification via teXt anchoring), a fusion framework that uses clinical text---encoded by a biomedical language model---as an institution-invariant semantic anchor. Because clinical variables follow standardized classification systems, their embeddings occupy a representation space that is consistent across institutions, providing a stable reference frame for aligning heterogeneous imaging features. CRUX introduces a text-mediated cross-modal bridge in which CT- and pathology-derived text-query summaries exchange information within a compact text semantic space through a learnable hub token, avoiding direct image-space cross-attention that would propagate domain-specific artifacts. We evaluate on 2,157 NSCLC patients from five hospitals, holding out one hospital (N=446) entirely for external validation. CRUX attains an external concordance index (C-index) of 0.761, the highest among all unimodal, bimodal, and trimodal configurations. Notably, direct CT-WSI fusion degrades under distribution shift, whereas text anchoring restores and improves cross-institutional generalization. These results indicate that language-model-encoded clinical text is an effective structured prior for robust, generalizable multimodal cancer prognosis.

eess.IV↗

arXiv2026

Development and Initial Performance of an Upgraded NaI(Tl) Crystal Encapsulation for COSINE-100U

The COSINE-100 experiment was designed to test the DAMA/LIBRA annual-modulation claim using low-background NaI(Tl) detectors. For the COSINE-100U upgrade, we developed a new crystal-encapsulation system to increase light-collection efficiency while preserving long-term detector stability, thereby improving sensitivity to low-mass dark matter. The upgraded design eliminates the quartz optical windows used in COSINE-100 and directly couples the photomultiplier tubes (PMTs) to the crystal end faces through 2-mm-thick silicone optical pads, thereby reducing the number of optical interfaces. For the larger crystals, the crystal edges were beveled to guide scintillation light more efficiently onto 3-inch high-quantum-efficiency PMTs. The performance study uses 2462~h (102.6~days) of room-temperature COSINE-100U data and, for direct background comparisons, reference COSINE-100 data acquired near the end of operation. 698~h (29.1~days) of COSINE-100 data acquired near the end of operation in March 2023. All eight crystals showed higher light yields than in COSINE-100, with values ranging from 15.8 to 27.7~p.e./keV; six crystals exceeded 20~p.e./keV. The measured bulk-$α$ rates were lower than the COSINE-100 values and consistent with the expected time evolution of internal $^{210}$Pb, while the 1--2-MeV surface-$α$ rates were substantially reduced. The upgrade also restored two crystals that had previously been excluded from the COSINE-100 physics analysis because of poor optical performance. Independent validation tests demonstrated that the encapsulation remains mechanically robust and optically stable during long-term immersion in liquid scintillator at low temperature. This paper presents the encapsulation design, the room-temperature detector performance, and the reduction in surface-related backgrounds achieved at the Yemilab facility.

physics.ins-det↗

arXiv2026

MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality

Accurate survival prediction from multimodal medical data is essential for precision oncology, yet clinical deployment faces a persistent challenge: modalities are frequently incomplete due to cost constraints, technical limitations, or retrospective data availability. While recent methods attempt to address missing modalities through feature alignment or joint distribution learning, they fundamentally lack explicit modeling of the unique contributions of each modality as opposed to the information derivable from other modalities. We propose MUST (Modality-Specific representation-aware Transformer), a novel framework that explicitly decomposes each modality's representation into modality-specific and cross-modal contextualized components through algebraic constraints in a learned low-rank shared subspace. This decomposition enables precise identification of what information is lost when a modality is absent. For the truly modality-specific information that cannot be inferred from available modalities, we employ conditional latent diffusion models to generate high-quality representations conditioned on recovered shared information and learned structural priors. Extensive experiments on five TCGA cancer datasets demonstrate that MUST achieves state-of-the-art performance with complete data while maintaining robust predictions in both missing pathology and missing genomics conditions, with clinically acceptable inference latency.

cs.CV↗

arXiv2026

Validation of the COSINE-100U NaI(Tl) Encapsulation for Low-Temperature Operation in Liquid Scintillator

The COSINE-100U (upgrade) will enhance the sensitivity of the COSINE-100 dark matter search by operating the detector array immersed in liquid scintillator (LS) at $-30^oC$. To validate the detector design for these conditions, we constructed a module using the COSINE-100U encapsulation and performed a dedicated long-term stability study. The module was first monitored at room temperature for ~110 days in air, followed by a one-week immersion in LAB-based LS to verify initial compatibility. Upon confirming stable optical performance, the temperature was lowered to $-33^oC$. During approximately 150 days of continuous operation at low temperature, we observed no degradation in performance. These results demonstrate the chemical and mechanical robustness of the encapsulation, confirming its suitability for the COSINE-100U physics run.

physics.ins-det↗

arXiv2025

Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge

The automated analysis of the aortic vessel tree (AVT) from computed tomography angiography (CTA) holds immense clinical potential, but its development has been impeded by a lack of shared, high-quality data. We launched the SEG.A. challenge to catalyze progress in this field by introducing a large, publicly available, multi-institutional dataset for AVT segmentation. The challenge benchmarked automated algorithms on a hidden test set, with subsequent optional tasks in surface meshing for computational simulations. Our findings reveal a clear convergence on deep learning methodologies, with 3D U-Net architectures dominating the top submissions. A key result was that an ensemble of the highest-ranking algorithms significantly outperformed individual models, highlighting the benefits of model fusion. Performance was strongly linked to algorithmic design, particularly the use of customized post-processing steps, and the characteristics of the training data. This initiative not only establishes a new performance benchmark but also provides a lasting resource to drive future innovation toward robust, clinically translatable tools.

cs.CV↗

arXiv2025

New Constraints on Axion-Like Particles with the NEON Detector at a Nuclear Reactor

We report new constraints on axion-like particles (ALPs) using data from the NEON experiment, which features a 16.7 kg of NaI(Tl) target located 23.7 meters from a 2.8 GW thermal power nuclear reactor. Analyzing a total exposure of 3063 kg$\cdot$days, with 1596 kg$\cdot$days during reactor-on and 1467 kg$\cdot$days during reactor-off periods, we compared energy spectra to search for ALP-induced signals. No significant signal was observed, enabling us to set exclusion limits at the 95\% confidence level. These limits probe previously unexplored regions of the ALP parameter space, particularly for axion mass ($m_a$) near $1$ MeV/c$^2$. For ALP-photon coupling (${g_{aγ}}$), limits reach as low as 6.24$\times$ 10$^{-6}$ GeV$^{-1}$ at $m_a$ = 3.0 MeV/c$^2$, while for ALP-electron coupling (${g_{ae}}$), limits reach 4.95$\times$ 10$^{-8}$ at $m_a$ = 1.02 MeV/c$^2$. This work demonstrates the potential for future reactor experiments to probe unexplored ALP parameter space.

hep-ex↗

arXiv2024

CV-Attention UNet: Attention-based UNet for 3D Cerebrovascular Segmentation of Enhanced TOF-MRA Images

Due to the lack of automated methods, to diagnose cerebrovascular disease, time-of-flight magnetic resonance angiography (TOF-MRA) is assessed visually, making it time-consuming. The commonly used encoder-decoder architectures for cerebrovascular segmentation utilize redundant features, eventually leading to the extraction of low-level features multiple times. Additionally, convolutional neural networks (CNNs) suffer from performance degradation when the batch size is small, and deeper networks experience the vanishing gradient problem. Methods: In this paper, we attempt to solve these limitations and propose the 3D cerebrovascular attention UNet method, named CV-AttentionUNet, for precise extraction of brain vessel images. We proposed a sequence of preprocessing techniques followed by deeply supervised UNet to improve the accuracy of segmentation of the brain vessels leading to a stroke. To combine the low and high semantics, we applied the attention mechanism. This mechanism focuses on relevant associations and neglects irrelevant anatomical information. Furthermore, the inclusion of deep supervision incorporates different levels of features that prove to be beneficial for network convergence. Results: We demonstrate the efficiency of the proposed method by cross-validating with an unlabeled dataset, which was further labeled by us. We believe that the novelty of this algorithm lies in its ability to perform well on both labeled and unlabeled data with image processing-based enhancement. The results indicate that our method performed better than the existing state-of-the-art methods on the TubeTK dataset. Conclusion: The proposed method will help in accurate segmentation of cerebrovascular structure leading to stroke

eess.IV↗

arXiv2019

An experiment to search for dark matter interactions using sodium iodide detectors

Observations of galaxies and primordial radiation suggest that the Universe is made mostly of non-luminous dark matter. Several types of new fundamental particles have been proposed as candidates for dark matter such as weakly interacting massive particles (WIMPs) but no definitive signal has been seen despite concerted efforts by many collaborations. One exception is the much-debated claim by the DAMA collaboration of a statistically significant annual modulation in the event rate of their experiment with a period and phase consistent with that expected from WIMP dark matter. Several groups have been working to develop experiments with the aim of reproducing DAMA's results using the same target medium. Here we report results from the initial operation of the COSINE-100 experiment. COSINE-100 uses sodium iodide as the target medium-the same medium as DAMA-and is designed to carry out a model-independent test of DAMA's claim. Initial data based on the first 59.5 days indicate that there is no excess of events over the expected background, confirming that DAMA's annual modulation signal is in severe tension with results from other experiments under the assumption of dark matter having spin independent interactions and the Standard Halo Model. COSINE-100 is now taking data to study the presence of dark matter-induced annual modulation in the event rate of the sodium iodide detectors.

astro-ph.IM↗

arXiv2015

Status of the KIMS-NaI experiment

KIMS-NaI is a direct detection experiment searching for Weakly Interacting Massive Particles (WIMP) via their scattering off of nuclei in a NaI(Tl) crystal. The KIMS-NaI collaboration has carried out tests of six crystals in the Yangyang underground laboratory in order to develope low-background NaI(Tl) crystals. Studies of internal backgrounds crystals have been performed with the goal of reducing backgrounds levels to 1 dru at 2 keV. Pulse shape discrimination (PSD) capabilities were also investigated for distinguishing between WIMP nuclear recoil signals and electron recoil backgrounds. The PSD analysis was applied to underground data with one low background NaI(Tl) detector and the evaluation of WIMP mass limit is ongoing.

physics.ins-det↗