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Yunan Wu

Ph.D. Candidate

Education

Ph.D. in Electrical Computer Engineering

Northwestern University, Evanston, IL, USA, 60201
2020 - 2024 (Expected)
Advisor - Dr. Aggelos K. Katsaggelos
GPA - 3.89/4.00

M.S. in Biomedical Enineering

Northwestern University, Evanston, IL, USA, 60201
2018 - 2020
Advisor - Dr. Aggelos K. Katsaggelos
GPA - 3.88/4.00

BSc in Electrical Engineering

Southern Medical University, Guangzhou, Guangdong, China
2014 - 2018

Languages

  • English (Professional)
  • Chinese (Native)

News

Interests

Interests

  • Medical Signal and Imaging Processing (CT, X-Ray, MRI, Pathology)
  • Artificial Intelligence in Healthcare (Computer Aided Diagnosis/Prognosis, Precision Medicine)
  • Data Science (Stock, Economy, Astrophysics)
  • Machine Learning and Deep Learning (Un/Semi/Weakly Supervised Learning, Segmentations/Detection/Classification, Interpretation/Visualization)
  • Human Computer Interaction (Camera, Virtual Reality, Self-driving Car)

Internships

HCI Research Assistant

2022.06 - 2022.09
The Roux Institute, Portland, ME 04101

Advisor: Clifton Forlines, Ph.D.

Developed a cheaper and easier-to-use technology that can compete with expensive devices by providing researchers with physiological signals from sensors to measure users’ cognitive and emotional workload in real-time as they are engaged in a task. Details included:

  • Collected biometric measurements from Emotibit and Empatica E4 along with established EEG measurements from Emotiv.
  • Built Machine Learning models to map physiological signals to cognitive and emotional scores of excitement, focus, engagement and stress.
  • A conference paper was accepted in PerCom2023.

AI Medical Imaging Research Assistant

2019.11.11 - 2020.05.21
Rush University Medical Center, Chicago, IL 60612

Advisor: Jie Deng, Ph.D. and Mark Supanich, Ph.D.

Developed a deep learning-based workflow to assist radiologists in diagnosing diseases quickly and accurately, thereby advancing the development of artificial intelligence imaging healthcare.

  • Knee injury, developed convolutional neural networks to classify anterior cruciate ligament (ACL) tear.
  • Breast tumor, designed a computer aided system with evidence-based confidence level analyses to detect malignant breast tumors.
  • Liver tumor, developed a deep learning model to differentiate levels of malignant liver tumors.

Journal Publications

  • A deep learning method for predicting the COVID-19 ICU patient outcome fusing X-rays, respiratory sounds, and ICU parameters
    • Wu Y, Rocha BM, Kaimakamis E, Cheimariotis GA, Petmezas G, Chatzis E, Kilintzis V, Stefanopoulos L, Pessoa D, Marques A, Carvalho P
    Expert Systems with Applications. 2024 Jan 1;235:121089.
  • Gravity Spy - Lessons Learned and a Path Forward.
    • Zevin M, Jackson CB, Doctor Z, Wu Y, Østerlund C, Johnson LC, Berry CP, Crowston K, Coughlin SB, Kalogera V, Banagiri S.
    arXiv preprint arXiv:2308.15530. 2023 Aug 29.
  • DeepCOVID-Fuse - A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels.
    • Wu Y, Dravid A, Wehbe RM, Katsaggelos AK
    Bioengineering. 2023 May 5;10(5):556.
  • A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction.
    • Wu Y, Besson P, Azcona EA, Bandt SK, Parrish TB, Breiter HC, Katsaggelos AK
    Scientific reports. 2022 Oct 22;12(1):1-16.
  • Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection.
    • López-Pérez M, Schmidt A, Wu Y, Molina R, Katsaggelos AK
    Computer Methods and Programs in Biomedicine. 2022 Jun 1;219:106783.
  • A deep learning fusion model with evidence-based confidence level analysis for differentiation of malignant and benign breast tumors using dynamic contrast enhanced MRI.
    • Wu Y, Wu J, Dou Y, Rubert N, Wang Y, Deng J
    Biomedical Signal Processing and Control. 2022 Feb 1;72:103319.
  • Can Deep Learning Assist Automatic Identification of Layered Pigments From XRF Data?.
    • Xu B, Wu Y, Hao P, Vermeulen M, McGeachy A, Smith K, Rayner G, Eremin K, Verri G, Willomitzer F, Alfeld M
    Journal of Analytical Atomic Spectrometry. 2022.
  • Ensembled deep neural network for intracranial hemorrhage detection and subtype classification on noncontrast ct images.
    • Wu Y, Supanich MP, Jie D.
    Journal of Artificial Intelligence for Medical Sciences. 2021 May 5;2(1-2):12-20.
  • DeepCOVID-XR: an artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large US clinical data set.
    • Wehbe RM, Sheng J, Dutta S, Chai S, Dravid A, Barutcu S, Wu Y, Cantrell DR, Xiao N, Allen BD, MacNealy GA.
    Radiology. 2021 Apr;299(1):E167.
  • Deep learning LI-RADS grading system based on contrast enhanced multiphase MRI for differentiation between LR-3 and LR-4/LR-5 liver tumors.
    • Wu Y, White GM, Cornelius T, Gowdar I, Ansari MH, Supanich MP, Deng J.
    Annals of Translational Medicine. 2020 Jun;8(11).
  • Discriminative Dimensionality Reduction using Deep Neural Networks for Clustering of LIGO Data.
    • Bahaadini S, Wu Y, Coughlin S, Zevin M, Katsaggelos AK.
    [Under review] arXiv preprint arXiv:2205.13672. 2022 May 26.
  • Analyzing Brain Morphology in Alzheimer’s Disease Using Discriminative and Generative Spiral Networks.
    • Azcona EA, Besson P, Wu Y, Kurani AS, Bandt SK, Parrish TB, Katsaggelos AK, Alzheimer’s Disease Neuroimaging Initiative.
    [Under review] bioRxiv. 2021 Jan 1.

    Conference Publications

  • Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection.
    • Wu Y, Castro-Macías FM, Morales-Álvarez P, Molina R, Katsaggelos AK.
    InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 327-337). Cham: Springer Nature Switzerland.
  • Cognitive and Emotional Monitoring with Inexpensive Wrist-Worn Consumer-Grade Wearables.
    • Wu Y, Valdez R, Forlines C
    In2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) 2023 Mar 13 (pp. 665-670). IEEE.
  • Investigating the Potential of Auxiliary-Classifier Gans for Image Classification in Low Data Regimes.
    • Dravid A, Schiffers F, Wu Y, Cossairt O, Katsaggelos AK
    InICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2022 May 23 (pp. 3318-3322). IEEE.
  • Reconstruction of Resting State FMRI Using LSTM Variational Auto-Encoder on Subcortical Surface to Detect Epilepsy.
    • Wu Y, Besson P, Azcona EA, Bandt SK, Parrish TB, Katsaggelos AK
    In2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) 2022 Mar 28 (pp. 1-5). IEEE.
  • Motion artifact reduction in abdominal MRIs using generative adversarial networks with perceptual similarity loss.
    • Wu Y, Wang X, Katsaggelos AK
    In17th International Symposium on Medical Information Processing and Analysis 2021 Dec 10 (Vol. 12088, pp. 142-150). SPIE.
  • Combining attention-based multiple instance learning and gaussian processes for CT hemorrhage detection.
    • Wu Y, Schmidt A, Hernández-Sánchez E, Molina R, Katsaggelos AK
    InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2021 Sep 27 (pp. 582-591). Springer, Cham.
  • Go-selfies: A Fast Selfies Background Removal Method Using ResU-Net Deep Learning.
    • Wu Y
    In2020 28th European Signal Processing Conference (EUSIPCO) 2021 Jan 18 (pp. 615-619). IEEE.
  • Interpretation of brain morphology in association to alzheimer’s disease dementia classification using graph convolutional networks on triangulated meshes.
    • Azcona EA, Besson P, Wu Y, Punjabi A, Martersteck A, Dravid A, Parrish TB, Bandt SK, Katsaggelos AK.
    InInternational Workshop on Shape in Medical Imaging 2020 Oct 4 (pp. 95-107). Springer, Cham.
  • A comparison of 1-D and 2-D deep convolutional neural networks in ECG classification.
    • Wu Y, Yang F, Liu Y, Zha X, Yuan S.
    InInternational Conference of the IEEE Engineering in Medicine and Biology Society 2018 (pp. 324-327). IEEE.

    Presentations

    - Identification of Intracranial Hemorrhage and Its Subtypes on Head CT Scans Using Transfer Learning and Weakly Supervised Networks. - American Society of Neuroradiology (ASNR) 2022, New York City, Oral presentation.
    - Reconstruction of Resting State fMRI Using LSTM Variational Auto-encoder On Subcortical Surface to Detect Epilepsy. - The 19th IEEE International Symposium on Biomedical Imaging (ISBI) 2022, Kolkata, India, Poster presentation.
    - Combining Attention-Based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection. - The 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021, Strasbourg, France, Poster presentation.
    - Motion artifact reduction in abdominal MRIs using generative adversarial networks with perceptual similarity loss. - The 17th International Symposium on Medical Information Processing and Analysis (SIPAIM), 2021, Virtual, Oral presentation.
    - Go-selfies: A Fast Selfies Background Removal Method Using ResU-Net Deep Learning. - The 28th European Signal Processing Conference (EUSIPCO), 2020, Amsterdam, Netherlands, Oral Presentation.
    - Automatic Identification of Emergent Findings on Head CT Scan using Deep Learning. - American Society of Neuroradiology (ASNR) 2021, Chicago, Oral presentation.
    - Geometric Deep Learning on Brain Morphology to Predict Composite Score of Fluid Cognition. - Radiological Society of North America (RSNA) 2020, Virtual, Oral presentation.
    - Deep Learning Method for Intracranial Hemorrhage Detection and Subtype Differentiation. - The 17th IEEE International Symposium on Biomedical Imaging (ISBI) 2020, Iowa City, USA, Poster Presentation.
    - Fast Breast Cancer MRI Screening Using a Deep Learning Model Combined with Analytical Imaging Features. - American Roentgen Ray Society (ARRS) 2020, Chicago, USA, Poster Presentation
    - A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification. - The 40th International Conference of the IEEE in Engineering Medicine and Biology Society (EMBS) 2018, Honolulu, USA, Poster Presentation.

    Skills & Proficiency

    Python

    Tensorflow & Pytorch & Keras

    R & SPSS & MATLAB

    HTML5 & CSS

    Sketch & Adobe & Microsoft

    JAVA & C++