東京科学大学総合研究院 M&Dデータ科学センター
Institute of Integrated Research M&D Data Science Center, Science Tokyo

Department of AI Technology Development

Home » Research Divisions/Departments » AI Technology Development

Member

Xin Zhu

Professor

Takashi Kamatani

Junior Associate Professor

Research Topics

The mission of Department of AI Technology Development is to develop advanced artificial intelligence (AI) and machine learning (ML) technologies to analyze medical and healthcare big data to extract complicated systematic information related to adult diseases.

 

Research contents

Medical big-data analysis and evidence-based personalized medicine are crucial to settling social issues, such as the population decline and super-aging challenge. The mission of Department of AI Technology Development is to develop machine learning and artificial intelligence methodologies that can be used in biomedical research and to identify evidence in precision medicine based on biomedical data analysis.
The main research areas of the Department of AI Technology Development include: 1. Development of the machine learning and artificial intelligence methodologies that can be used in biomedical research. 2. Development of AIoT (AI+Internet of Things(IoT)) medical devices, surgical operation support&training systems based on virtual reality.

 

(1) MI and AI technology for personal medicine

  1. Study on the prognostic analysis of patients with heart failure using electronic medical records
  2. Study on the prognostic analysis of patients with colon cancer using electronic medical records

 

Figure 1 The prognostic analysis of patients with heart failure using machine learning and electronic medical records. (adapted from Zhou, Zhu, and et al. Life 2022)

 

Development of AI technology for the analysis of clinical medical images

(1) The analysis of endoscopy images and videos

(2) The analysis of CT, MRI, and ultrasound images

(3) The analysis of digital pathological images.

Figure 2 Recognition of swallowing disorders using contrastive learning and 3D convolutional neural networks in Frexible Endoscopic Evaluation of Swallow:FEES. (adapted from WENG and ZHU, IEEE ISBI 2024)

Publications

  1. Weng W., Zhu X., Alaya Cheikh F., Ullah M., Imaizumi M., and Murono S. A Simple Framework for Depth-Augmented Contrastive Learning for Endoscopic Image Classification, IEEE Transactions on Instrumentation and Measurement, vol.73, 2024
  2. Li Q., ZHU X., and CHEN W. Parallelization of Three Dimensional Cardiac Simulation on GPU, Biomedicines,12(9),2126,2024
  3. Nakajima Y, …, Togashi K, and ZHU X.“Differences in Regions-Of-Interest to Identify Deeply Invasive Colorectal Cancers: Computer-Aided Diagnosis Compared with Endoscopists”, Endoscopy International Open, online publication.
  4. Sun Y, Zhu X, Chen W, Weng W, and Nakamura K. “Computer Simulation of Low-Power and Long-Duration Bipolar Radiofrequency Ablation Under Various Baseline Impedances”, Medical Engineering & Physics, 131, 104226, 2024.
  5. Zhao Y, Dohi O, Ishida T, Yoshida N, Ochiai T, Mukai H, Seya M, Yamauchi K, Miyazaki H, Fukui H, Yasuda T, Iwai N, Inoue K, Itoh Y, Liu X, Zhang R, Zhu X. “Linked color imaging with artificial intelligence improves the detection of early gastric cancer”, Digestive Diseases, Aug. 2024, Published online.
  6. Sugino S, Yoshida N, Guo Z, Zhang R, Inoue K, Hirose R, Dohi O, Itoh Y, Nemoto D, Togashi K, Yamamoto H, Zhu X. “Non-polypoid Colorectal Lesions Detection and False Positive Detection by Artificial Intelligence under Blue Laser Imaging and Linked Color Imaging”, Journal of the Anus, Rectum and Colon, vol. 8, no. 3, pp. 212-220, 2024.
  7. Peng B, Liu Y, Wang WW, Zhou Q, Li F, and Zhu X, “Bidirectional Copy-Paste Mamba for Enhanced Semi-Supervised Segmentation of Transvaginal Uterine Ultrasound Images”, Diagnostics, vol.14, 1423, 2024. IF: 3.0(2023)
  8. Weng W, Yoshida N, Morinaga Y, Sugino S, Tomita Y, Kobayashi R, Inoue K, Hirose R, Dohi O, Itoh Y, and Zhu X, “Development of high-quality artificial intelligence for computer-aided diagnosis in determining subtypes of colorectal cancer”, Journal of Gastroenterology and Hepatology, Published online, June, 2024. IF: 4.0(2023)
  9. Song Bai, Jiang H, Liu J, Yu Y, Luan J, Zhao Y, Wang Y, Zhang J, Liu Z, Zhang N, Zhu X, and Ma Z, Deep Learning-assisted Real-time Wall Shear Stress Measurement on Chicken Embryo Heart Using Spectral Domain Optical Coherence Tomography, IEEE Transactions on Instrumentation and Measurement, June, 2024. IF: 5.6(2023)
  10. Sharma AK, Liu SH, Zhu X and Chen W, Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning, Electronics, 13(9), 1791, 2024. IF: 2.6(2023)
  11. Kobayashi R, Yoshida N, Tomita Y, Hashimoto H, Inoue K, Hirose R, Dohi O, Inada Y, Murakami T, Morimoto Y, Zhu X, Itoh Y, Detailed Superiority of the CAD EYE Artificial Intelligence System over Endoscopists for Lesion Detection and Characterization Using Unique Movie Sets, Journal of the Anus, Rectum and Colon, 8(2), 61-69, 2024. IF: 1.6(2023)
  12. Liu X, Zhu X, Tian X, Iwasaki T, Sato A and Kazama JJ, Renal Pathological Image Classification Based on Contrastive and Transfer Learning, Electronics, 13(7), 1403, 2024. IF: 2.6(2023)
  13. Imaizumi M, Weng W, Zhu X, and Murono S, “Effectiveness of FEES with artificial intelligence-assisted computer-aided diagnosis”, Auris Nasus Larynx, vol. 51, 251-258, 2024. IF: 1.7(2022)
  1. Tang Z, Jiang L, Zhu X, and Huang M, “An Internet of Things-Based Home Telehealth System for Smart Healthcare by Monitoring Sleep and Water Usage: A Preliminary Study”, Electronics, vol. 12, 3652, 2023. IF: 2.9(2022)
  2. Chin C, Lin C, Wang J, Chin W, Chen Y, Chang S, Huang P, Zhu X, Hsu Y, Liu S, “A Wearable Assistant Device for Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing”, Sensors, vol.23, 7454, 2023. IF: 3.9(2022)
  3. Wang J, Liu S, Tsai C, Manousakas I, Zhu X, and Lee T, “Signal Quality Analysis of Single-Arm Electrocardiography”, Sensors, vol.23, 5818, 2023. IF:3.9(2022)
  4. Zhao A, Du X, Yuan S, Shen W, Zhu X, and Wang WW, “Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning”, Diagnostics, vol.13, 1409, 2023. IF: 3.6(2022)
  5. Nemoto D, Guo Z, Katsuki S, Takezawa T, Maemoto R, Kawasaki K, Inoue K, Akutagawa T, Tanaka H, Sato K, Omori T, Takanashi K, Hayashi Y, Nakajima Y, Miyakura Y, Matsumoto T, Yoshida N, Esaki M, Uraoka T, Kato H, Inoue Y, Peng B, Zhang R, Hisabe T, Matsuda T, Yamamoto H, Tanaka N, Lefor AK, Zhu X, and Togashi K, “Computer-Aided Diagnosis of Early-Stage Colorectal Cancer Using Non-Magnified Endoscopic White Light Images”, Gastrointestinal Endoscopy, vol.98, 90-99, 2023. IF: 7.7(2022)
  6. Weng W, Zhu X, Jing L, and Dong M “Attention Mechanism Trained with Small Datasets for Biomedical Image Segmentation”, Electronics, vol. 12, 682, 2023. IF: 2.9(2022) Kudo S, Chen Z, Zhou X, Izu LT, Chen-Izu Y, Zhu X, Tamura T, Kanaya S, Huang M, “A training pipeline of an arrhythmia classifier for atrial fibrillation detection using Photoplethysmography signal”, Frontiers in Physiology, vol. 14, 1084837, 2023. IF: 4.0(2022)
  7. Chen Z, Yang Z, Wang D, Zhu X, Ono N, Altaf-Ul-Amin MD, Kanaya S, Huang M, “Sleep Staging Framework with Physiologically Harmonized Sub-Networks”, Methods, vol.209, 18-28, 2023. IF: 4.8(2022)
  8. Sun Y, Zhu X, Nakamura K, and Wang S, “Evaluation of lesion characteristics and baseline impedance on high-power short-duration radiofrequency catheter ablation using computer simulation”, Heart and Vessels, vol. 38, 1459-67, 2023. IF: 1.6(2022)
  9. Ikeda N, Kubota H, Suzuki R, Morita M, Yoshimura A, Osada Y, Kishida K, Kitamura D, Iwata A, Yotsumoto S, Kurotaki D, Nishimura K, Tamura T, Kamatani T, Tsunoda T, Murakawa Miyako, Asahina Y, Hayashi Y, Harada H, Harada Y, Yokota A, Hirai H, Seki T, Kuwahara M, Yamashita M, Shichino S, Tanaka M, Asano K. The early neutrophil-committed progenitors aberrantly differentiate into immunoregulatory monocytes during emergency myelopoiesis. Cell Reports (2023 Accepted.).
  10. H. Park, S. Imoto and S. Miyano.GRN-classifier: Gene regulatory network-based classifier and its applications to gastric cancer drug (5-FU) marker identification.Journal of Computational Biology, In press.
  1. H. Park, S. Imoto and S. Miyano.PredictiveNetwork: predictive gene network estimation with application to gastric cancer drug response-predictive network analysis. BMC Bioinformatics, 23(1):342. (2022)
  2. H. Park, R. Yamaguchi, S. Imoto and S. Miyano.Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.PLoS One, 17(5):e0261630. (2022)
  3. H. Park, R. Yamaguchi, S. Imoto and S. Miyano.Uncovering Molecular Mechanisms of Drug Resistance via Network-Constrained Common Structure Identification. Journal of Computational Biology, 29(3):257-275. (2022)
  4. Kameyama N, Sato T, Arai D, Fujisawa D, Takeuchi M, Nakachi I, Kawada I, Yasuda H, Ikemura S, Terai H, Nukaga S, Nakano Y, Hirano T, Minematsu N, Asakura T, Kamatani T, Tanaka K, Suzuki S, Miyawaki M, Naoki K, Fukunaga K, Soejima K. Most important things and associated factors with prioritizing ‘daily life’ in patients with advanced lung cancer. JCO Oncology Practice, 18, e1977-86 (2022).
  5. Baba R, Kabata H, Shirasaki Y, Kamatani T, Yamagishi M, Irie M, Watanabe R, Matsusaka M, Masaki K, Miyata J, Moro K, Uemura S, Fukunaga K. Upregulation of IL-4 receptor signaling pathway in circulating ILC2s from asthma patients. The Journal of Allergy and Clinical Immunology: Global, 1, 299-304 (2022).
  6. Matsuo H, Kamatani T, Hamba Y, Boroevich KA, Tsunoda T. Association between high immune activity and worse prognosis in uveal melanoma and low-grade glioma in TCGA transcriptomic data. BMC Genomics, 23, 351 (2022).
  7. Sugawara T, Miya F, Ishikawa T, Lysenko A, Nishino J, Kamatani T, Takemoto A, Boroevich KA, Kakimi K, Kinugasa Y, Tanabe M, Tsunoda T. Immune subtypes and neoantigen-related immune evasion in advanced colorectal cancer. iScience, 25, 103740 (2022)
  1. Weng W, Imaizumi M, Murono S and Zhu X, “Expert level aspiration and penetration detection during flexible endoscopic evaluation of swallowing with artificial intelligence assisted diagnosis”, Scientific Reports, vol. 12, 21689, 2022. IF: 4.997(2021)
  2. Nakamura K, …., Zhu X. Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning, Diagnostics, vol.12, 2947, 2022. IF: 3.992(2021)
  3. Liu S, Yang Z, Pan K, Zhu X, and Chen W, Estimation of Left Ventricular Ejection Fraction Using Cardiovascular Hemodynamic Parameters and Pulse Morphological Characteristics with Machine Learning Algorithms, Nutrients, vol.14, 4051, 2022. IF: 6.706(2021)
  4. Nemoto D, Guo Z, Peng B, Zhang R, Nakajima Y, Hayashi Y, Yamashina T, Aizawa M, Utano K, Lefor AK, Zhu X, Togashi K, Computer-Aided Diagnosis of Serrated Colorectal Lesions Using Non-Magnified White Light Endoscopic Images, International Journal of Colorectal Disease, vol. 37, 1875-1884, 2022. IF: 2.796(2021)
  5. Zhou X, Nakamura K, Sahara N, Asami M, Toyoda Y, Enomoto Y, Hara H, Noro M, Sugi K, Masao Moroi, Masato Nakamura, Huang M and Zhu X, Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning, Life, vol. 12, 776, 2022. IF: 3.253(2021)
  6. Ma Z, Ding N, Li Z, Zhu K, Li A, Lin Z, Wang Y, Zhao Y, Yu Y, Luan J, Zhu X, and Liu J, “Spectral interference contrast based non-contact photoacoustic microscopy realized by SDOCT”, Optics Letters, vol. 47, 2895-98, 2022. IF:3.832(2021)
  7. Lin Y, Yu M, Wang Y, Meng Z, Li A, He Z, Wang Q, Liu J, Yu Y, Zhao Y, Zhu X and Ma Z, “High-speed all-optic optical coherence tomography and photoacoustic microscopy dual-modal system for microcirculation evaluation”, Journal of Innovative Optical Health Sciences, 2250023,April 2022. IF: 2.396(2021)
  8. Liu Y, Zhou Q, Peng B, Jiang J, Fang Li, Weng W, Wang WW, Wang S and Zhu X, “Automatic Measurement of Endometrial Thickness from Transvaginal Ultrasound Images”, Frontiers in Bioengineering and Biotechnology, vol. 10, 853845, 2022. IF: 6.064(2021)
  9. Wang WW, Xu Y, Yuan S, Li Z, Zhu X, Zhou Q, Shen W and Wang S, “Prediction of endometrial carcinoma using the combination of electronic health records and an ensemble machine learning method”, Frontiers in Medicine, vol. 9, 851890, 2021. IF: 5.058(2021)
  10. Liu J, Yan S, Lu N, Yang D, Lv H, Wang S, Zhu X, et al, “Automated Retinal Boundary Segmentation of Optical Coherence Tomography Images Using an Improved Canny Operator”, Scientific Reports, vol. 12, 1412, 2022. IF: 4.996(2021)
  11. Liu J, Yan S, Lu N, Yang D, Fan C, Lv H, Wang S, Zhu X, et al. “Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images”, Journal of Innovative Optical Health Sciences, vol. 15, 2242001, 2022. IF: 2.396(2021)
  12. Hakozaki K, Tanaka N*, Takamatsu K, Takahashi R, Yasumizu Y, Mikami S, Shinojima T, Kakimi K, Kamatani T, Miya F, Tsunoda T, Aimono E, Nishihara H, Mizuno R & Oya M. Landscape of prognostic signatures and immunogenomics of the AXL/GAS6 axis in renal cell carcinoma. BR. J. Cancer, 125, 1533-43 (2021)
  13. Takamatsu K, Tanaka N*, Hakozaki K, Takahashi R, Teranishi Y, Murakami T, Kufukihara R, Niwa N, Mikami S, Shinojima T, Sasaki T, Sato Y, Kume H, Ogawa S, Kakimi K, Kamatani T, Miya F, Tsunoda T, Aimono E, Nishihara H, Sawada K, Imamura T, Mizuno R, and Oya M. Profiling the inhibitory receptors LAG-3, TIM-3, and TIGIT in renal cell carcinoma reveals malignancy. Nat. Commun., 12, 5547 (2021).
  14. Ishioka K, Yasuda H, Hamamoto J, Terai H, Emoto K, Kim TJ, Hirose S, Kamatani T, Mimaki S, Arai D, Ohgino K, Tani T, Masuzawa K, Manabe T, Shinozaki T, Mitsuishi A, Ebisudani T, Fukushima T, Ozaki M, Ikemura S, Kawada I, Naoki K, Nakamura M, Ohtsuka T, Asamura H, Tsuchihara K, Hayashi Y, Hegab AE, Kobayashi SS, Kohno T, Watanabe H, Ornitz DM, Betsuyaku T, Soejima K, Fukunaga K. Upregulation of FGF9 in Lung Adenocarcinoma Transdifferentiation to Small Cell Lung Cancer. Cancer Res., 81, 3916-29 (2021)
  15. DU R, Xie S, Fang Y, Igarashi-Yokoi T, Moriyama M, Ogata S, Tsunoda T, Kamatani T, Yamamoto S, Cheng CY, Saw SM, Ting D, Wong TY, Ohno-Matsui K. Deep Learning Approach for Automated Detection of Myopic Maculopathy and Pathologic Myopia in Fundus Images. Ophthalmol. Retinal., S2468-6530 (2021).
  16. Nishiguchi KM*, Miya F* (equal contribution), Fujita K, Akiyama M, Takigawa T, Kamatani T, Koyanagi Y, Ueno S, Tsugita M, Kunikata H, Cisarova K, Nishino J, Murakami A, Abe T, Momozawa Y, Terasaki H, Wada Y, Sonoda K, Rivolta C, Ishibashi T, Tsunoda T, Tsujikawa M, Ikeda Y, Nakazawa T. A hypomorphic variant in EYS detected by genome-wide association study contributes toward retinitis pigmentosa. Commun. Biol., 29, 140 (2021).
  17. H. Park, K. Maruhashi, R. Yamaguchi, S. Imoto and S. Miyano.Global gene network exploration based on explainable artificial intelligence approach. PLoS One, 15(11):e0241508. (2020)
  18. H. Park, R. Yamaguchi, S. Imoto and S. Miyano.Automatic sparse principal component analysis.Canadian Journal of Statistics, 49(3):678-697. (2020)
  19. Sato Y, Wada I, Odaira K, Kobayashi Y, Hosoi A, Nagaoka K, Karasaki T, Matsushita H, Yagi K, Yamashita H, Fujita M, Watanabe S, Kamatani T, Miya F, Mineno J, Nakagawa H, Tsunoda T, Takahashi S, Seto Y, Kakimi K. Integrative immunogenomic analysis of gastric cancer dictates novel immunological classification and the functional status of tumor-infiltrating cells. Clin. Transl. Immunology 9, e1194 (2020).
  20. Masaki K, Miyata J, Kamatani T, Tanosaki T, Mochimaru T, Kabata H, Suzuki Y, Asano K, Betsuyaku T, Fukunaga K. Risk factors for poor adherence to inhaled corticosteroid therapy in patients with moderate to severe asthma. Asian Pac J Allergy Immunol. 2020
  21. Nishino J, Watanabe S, Miya F, Kamatani T, Sugawara T, Boroevich KA, Tsunoda T. Quantification of multicellular colonization in tumor metastasis using exome sequencing data. Int. J. Cancer, 146, 2488-2497 (2020).
  1. Weihao Weng, Xin Zhu, Learning Representations by Maximizing Mutual Information Across Views for Medical Image Segmentation, 27th INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION (MICCAI), Marrakesh, 2024, Oct. 6-10th.
  2. Weihao Weng, Xin Zhu, Mitsuyoshi Imaizum, Shigeyuki Murono. BAT: Behavior-aware Temporal Contrastive Video Representation Learning, The 21st IEEE International Symposium on Biomedical Imaging, Athenes, 2024, May. 27-30th.
  3. Qin Li, Xin Zhu, Wenfeng Shen, An Approach to Accelerate Three-dimensional Cardiac Simulation on GPU, 2023 IEEE 13th International Workshop on Computational Intelligence and Applications (IWCIA), Hiroshima, 2023, Nov. 11-12
  4. Xue Zhou, Xin Zhu, Tianhui Li, Keijiro Nakamura, and Ming Huang, Sex Difference in Atrial Fibrillation Recurrence After Catheter Ablation, The 12th International Conference on Awareness Science and Technology, Taichung, Taiwan, 2023, Nov. 9-11.
  5. Qin Li, Xin Zhu, and Wenfeng Shen, Approximation of Hodgkin-Huxley Model Using Neural Networks, The 12th International Conference on Awareness Science and Technology, Taichung, Taiwan, 2023, Nov. 9-11.
  6. Xinkai Liu, Xingjian Tian, Xin Zhu, Iwasaki Tsuyoshi, Atsuya Sato, and Junichiro Kazama, Semi-supervised Contrast Learning in Renal Pathological Image Classification, The 12th International Conference on Awareness Science and Technology, Taichung, Taiwan, 2023, Nov. 9-11.
  7. Ruiyao Zhang, Naohisa Yoshida, Osamu Dohi, Xingjian Tian, Xinkai Liu, and Xin Zhu, Performance and Domain Adaptability of Object Detection Algorithms in Processed Colonoscopy Videos, The 12th International Conference on Awareness Science and Technology, Taichung, Taiwan, 2023, Nov. 9-11.
  8. Yiyang Liu, Boyuan Peng, Ziwei Liang, Hiromasa Hayama, Keijiro Nakamura, and Xin Zhu, Automatic Edge Detection in Echocardiographic Doppler Images Using an Improved Canny Algorithm, The 12th International Conference on Awareness Science and Technology, Taichung, Taiwan, 2023, Nov. 9-11.
  9. Weihao Weng, Xin Zhu, Mitsuyoshi Imaizumi, Shigeyuki Murono, FEES-IS: Real-time Instance Segmentation of Flexible Endoscopic Evaluation of Swallowing, 11th European Workshop on Visual Information Processing (EUVIP), Gjovik, Norway, 2023, Sep. 11-14
  10. Yiyang Liu, Boyuan Peng, Xin Zhu, Wenwen Wang, Qin Zhou, Shixuan Wang, Jingjing Jiang, and Li Fang“Automatic endometrial segmentation in ultrasound images using deep learning”, 2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC). Penang, Malaysia, 2022, Dec. 19-22.
  11. Xue Zhou, Xin Zhu, and Keijiro Nakamura, Ming Huang, “Gender difference in prognosis of patients with heart failure: A propensity score matching analysis”, IEEE-EMBS International Conference on Biomedical and Health Informatics. Ioannina, Greece, 2022, Sep. 27-30.
  12. Boyuan Peng, Yiyang Liu, Xin Zhu, Shouhei Ikeda, and Saburo Tsunoda, “Segmentation of femur from MRI images using PP-LiteSeg”, IEEE-EMBS International Conference on Biomedical and Health Informatics. Ioannina, Greece, 2022, Sep. 27-30.
  13. Xue Zhou, Xin Zhu, and Keijiro Nakamura, Prediction of Hospitalization Cost and Length of Stay for Patients with Heart Failure Using Deep Learning, Proc. 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech 2022), Osaka, Mar. 7-9th, 2022.
  14. 鎌谷高志　第71回日本アレルギー学会学術大会臨床研究支援プログラム受賞者講演 「医師主体のAI解析を成功させるために〜呼吸器疾患を中心に〜」2022年　東京
  15. 鎌谷高志　第19回関東骨軟部腫瘍の基礎を語る会 「生物情報科学の視点から行うがん研究 ~DNA, RNA, 病理画像を用いた免疫環境解析や多領域解析を中心に~」2021年　東京
  1. Weihao Weng and Xin Zhu. "Popular vote" special prize, Advancing Health Equity Through Accessible Skin Disease Detection, 2024 IEEE Symposium of Bioimaging (ISBI2024), May 30th, 2024
  2. Weihao Weng, Xin Zhu and et al. The Most Excellent Paper Award, FEES-IS: Real-time Instance Segmentation of Flexible Endoscopic Evaluation of Swallowing, The 11th European Workshop on Visual Information Processing (EUVIP2023), Sep. 14th, 2023
  3. 2020年度JST AIPチャレンジ成果報告会　AIPネットワークラボ長賞（優秀賞）

Department of Integrated Analytics

Department of Biostatistics

Department of Data Science Algorithm Design and Analysis

Department of AI Systems Medicine