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2. Prognosis Score System to Predict Survival for COVID-19 Cases: a Korean Nationwide Cohort Study

Author

Cho, Sung-Yeon, Park, Sung-Soo, Song, Min-Kyu, Bae, Young Yi, Lee, Dong-Gun, and Kim, Dong-Wook

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundAs the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care. ObjectiveIn this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed. MethodsWe retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio. ResultsAmong the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count

Published
2021
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3. Asciminib monotherapy in patients with chronic-phase chronic myeloid leukemia with the T315I mutation after ≥1 prior tyrosine kinase inhibitor: 2-year follow-up results

Author

Cortes, Jorge E., Sasaki, Koji, Kim, Dong-Wook, Hughes, Timothy P., Etienne, Gabriel, Mauro, Michael J., Hochhaus, Andreas, Lang, Fabian, Heinrich, Michael C., Breccia, Massimo, Deininger, Michael, Goh, Yeow Tee, Janssen, Jeroen J.W.M., Talpaz, Moshe, de Soria, Valle Gomez Garcia, le Coutre, Philipp, DeAngelo, Daniel J., Damon, Andrea, Cacciatore, Silvia, Polydoros, Fotis, Agrawal, Nithya, and Rea, Delphine

Published
2024
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4. Clean realization of the Hund physics near the Mott transition: $\mathrm{NiS_2}$ under pressure

Author

Park, Ina, Jang, Bo Gyu, Kim, Dong Wook, Shim, Ji Hoon, and Kotliar, Gabriel

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Materials Science
Abstract

Strong correlation effects caused by Hund's coupling have been actively studied during the past decade. Hund's metal, strongly correlated while far from the Mott insulating limit, was studied as a representative example. However, recently, it was revealed that a typical Mott system also exhibits a sign of Hund physics by investigating the kink structure in the spectral function of $\mathrm{NiS_{2-x}Se_x}$. Therefore, to understand the Hund physics in a half-filled multi-orbital system near the metal-insulator transition, we studied pressure-induced metallic states of $\mathrm{NiS_2}$ by using density functional theory plus dynamical mean-field theory. Hund physics, responsible for suppressing local spin fluctuation, gives low-energy effective correlations, separated from Mott physics, which suppresses charge fluctuation at higher energy. This effect is prominent when $J$ becomes comparable to the quasiparticle kinetic energy, showing apparent scaling behavior of the kink position $E_{kink} \sim J \cdot Z$. We suggest that the Hund effect can also be observed in the optical conductivity as a non-Drude-like tail with $1/\omega$ frequency dependence and non-monotonic temperature evolution of the integrated optical spectral weight at a fixed frequency. Our study demonstrates the important role of Hund's coupling for electronic correlations even in a half-filled system., Comment: 10 pages, 5 figures with following supplementary material (7 pages, 5 figures)

Published
2023
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10. Morphology-Aware Interactive Keypoint Estimation

Author

Kim, Jinhee, Kim, Taesung, Kim, Taewoo, Choo, Jaegul, Kim, Dong-Wook, Ahn, Byungduk, Song, In-Seok, and Kim, Yoon-Ji

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Diagnosis based on medical images, such as X-ray images, often involves manual annotation of anatomical keypoints. However, this process involves significant human efforts and can thus be a bottleneck in the diagnostic process. To fully automate this procedure, deep-learning-based methods have been widely proposed and have achieved high performance in detecting keypoints in medical images. However, these methods still have clinical limitations: accuracy cannot be guaranteed for all cases, and it is necessary for doctors to double-check all predictions of models. In response, we propose a novel deep neural network that, given an X-ray image, automatically detects and refines the anatomical keypoints through a user-interactive system in which doctors can fix mispredicted keypoints with fewer clicks than needed during manual revision. Using our own collected data and the publicly available AASCE dataset, we demonstrate the effectiveness of the proposed method in reducing the annotation costs via extensive quantitative and qualitative results. A demo video of our approach is available on our project webpage., Comment: MICCAI 2022. The first two authors contributed equally. The last two authors are the co-corresponding authors

Published
2022

11. Growth monitoring of field-grown onion and garlic by CIE L*a*b* color space and region-based crop segmentation of UAV RGB images

Author

Kim, Dong-Wook, Jeong, Sang Jin, Lee, Won Suk, Yun, Heesup, Chung, Yong Suk, Kwon, Young-Seok, and Kim, Hak-Jin

Subjects
Unmanned aerial vehicle, Crop segmentation, CIE L*a*b* color space, Crop with long narrow leaves, Growth monitoring, Remote sensing, Crop and Pasture Production, Agronomy & Agriculture
Abstract

Canopy coverage-based crop growth monitoring is highly dependent on the performance of crop segmentation algorithms. Under field conditions, crop segmentation for unmanned aerial vehicle (UAV) imagery should be sophisticated considering geometric distortion of images by wind and illumination variations. Under Korean cultivation conditions, a plastic mulch used to restrict weeds and prevent cold weather damage increases the complexity of the image background. In particular, on-site monitoring of onion and garlic growth has been limited by their morphology because they have long narrow leaves. The ultimate goal of this study was to quantify the growth parameters of onion and garlic at multiple growth stages using red, green, and blue (RGB) imagery obtained with UAVs. Canopy coverage and plant height were used as predictor variables to develop mathematical models to estimate the fresh weights of onion and garlic. The use of a CIE L*a*b* color space and mean shift (MS) algorithm enhanced the extraction of the canopy coverage of onion and garlic from complex backgrounds, including plastic mulch, soil, and shadows under varying illumination conditions. Multiple linear regression models consisting of the a* band-based vegetation fraction (VF) and structure from motion (SfM)-based plant height (PH) fitted the fresh weight data of onion and garlic well with high coefficients of determination (R2) ranging from 0.82 to 0.92. The validation results showed an almost 1:1 slope with highly linear relationships (R2 > 0.82) between the onion and garlic fresh weights obtained with the UAV RGB imagery and actual fresh weights, confirming that the UAV-RGB imagery based on the use of the a*band and PH can be used to quantify the spatial and temporal variability of onion and garlic growth parameters during the growing season.

Published
2023

12. RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars

Author

Yoo, Jun-Sang, Kim, Dong-Wook, Lu, Yucheng, and Jung, Seung-Won

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent methods for single image super-resolution (SISR) have demonstrated outstanding performance in generating high-resolution (HR) images from low-resolution (LR) images. However, most of these methods show their superiority using synthetically generated LR images, and their generalizability to real-world images is often not satisfactory. In this paper, we pay attention to two well-known strategies developed for robust super-resolution (SR), i.e., reference-based SR (RefSR) and zero-shot SR (ZSSR), and propose an integrated solution, called reference-based zero-shot SR (RZSR). Following the principle of ZSSR, we train an image-specific SR network at test time using training samples extracted only from the input image itself. To advance ZSSR, we obtain reference image patches with rich textures and high-frequency details which are also extracted only from the input image using cross-scale matching. To this end, we construct an internal reference dataset and retrieve reference image patches from the dataset using depth information. Using LR patches and their corresponding HR reference patches, we train a RefSR network that is embodied with a non-local attention module. Experimental results demonstrate the superiority of the proposed RZSR compared to the previous ZSSR methods and robustness to unseen images compared to other fully supervised SISR methods., Comment: Accepted by IEEE Transactions on Multimedia (TMM)

Published
2022

23. Early dose reduction of dasatinib does not compromise clinical outcomes in patients with chronic myeloid leukemia: A comparative analysis of two prospective trials

Author

Shin, Dong-Yeop, Park, Sahee, Jang, Eunjung, Kong, Jee Hyun, Won, Young-Woong, Oh, Sukjoong, Choi, Yunsuk, Kim, Jeong-A, Lee, Se Won, Mun, Yeung-Chul, Kim, Hawk, Kim, Sung-Hyun, Rok Do, Young, Kwak, Jae-Yong, Kim, Hyeoung-Joon, Zang, Dae Young, Lim, Sung-Nam, Lee, Won Sik, and Kim, Dong-Wook

Published
2024
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38. Asciminib monotherapy in patients with CML-CP without BCR::ABL1 T315I mutations treated with at least two prior TKIs: 4-year phase 1 safety and efficacy results

Author

Mauro, Michael J., Hughes, Timothy P., Kim, Dong-Wook, Rea, Delphine, Cortes, Jorge E., Hochhaus, Andreas, Sasaki, Koji, Breccia, Massimo, Talpaz, Moshe, Ottmann, Oliver, Minami, Hironobu, Goh, Yeow Tee, DeAngelo, Daniel J., Heinrich, Michael C., Gómez-García de Soria, Valle, le Coutre, Philipp, Mahon, Francois-Xavier, Janssen, Jeroen J. W. M., Deininger, Michael, Shanmuganathan, Naranie, Geyer, Mark B., Cacciatore, Silvia, Polydoros, Fotis, Agrawal, Nithya, Hoch, Matthias, and Lang, Fabian

Published
2023
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40. Asciminib vs bosutinib in chronic-phase chronic myeloid leukemia previously treated with at least two tyrosine kinase inhibitors: longer-term follow-up of ASCEMBL

Author

Hochhaus, Andreas, Réa, Delphine, Boquimpani, Carla, Minami, Yosuke, Cortes, Jorge E., Hughes, Timothy P., Apperley, Jane F., Lomaia, Elza, Voloshin, Sergey, Turkina, Anna, Kim, Dong-Wook, Abdo, Andre, Fogliatto, Laura Maria, le Coutre, Philipp, Sasaki, Koji, Kim, Dennis Dong Hwan, Saussele, Susanne, Annunziata, Mario, Chaudhri, Naeem, Chee, Lynette, García-Gutiérrez, Valentin, Kapoor, Shruti, Allepuz, Alex, Quenet, Sara, Bédoucha, Véronique, and Mauro, Michael J.

Published
2023
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