Juan Shan

Juan Shan

Associate Professor
Seidenberg School of CSIS
Computer Science NY

Juan Shan

NYC
1104
15 Beekman
| Office Hours: Tue 3:30pm-5:00pm,Thu 1:30pm-5:00pm

Biography

Faculty Bio

Dr. Shan joined Pace University in August 2013. She is currently an Associate Professor in the Department of Computer Science. Prior to joining Pace, she was an Assistant Professor in the Department of Mathematics and Computer Science at Benedictine College from 2011 to 2013. Her primary research interest is the application of machine learning to medical image analysis and computer-aided diagnosis.

Awards and Honors

  • Pace University, 2016, Kenan Award
  • Pace University, 2014, Research Day 2014 Awardee
  • Intermountain Graduate Research Symposium, 2011, First Place Research Paper

Education

PhD, Utah State University, Logan, UT, 2011
Computer Science

BS, Harbin Institute of Technology, Harbin, China, 2004
Computer Science

Research and Creative Works

Research Interest

Dr. Shan's research interests include machine learning, medical image processing, and computer-aided diagnosis (CAD) systems. Her primary focus is developing robust and efficient CAD algorithms to help doctors analyzing medical images, discovering distinguishing features, and classifying data utilizing machine learning methods. Her on-going research projects include CAD systems for breast cancer, diabetic retinopathy, and knee osteoarthritis.

Courses Taught

Past Courses

CIS 101: Introduction to Computing
CIT 314: Introduction to Programming II
CS 113: Mathematical Structures for CS
CS 121: Computer Programming I
CS 121: Intro to Computer Science
CS 122: Computer Programming II
CS 122: Object-Oriented Programming
CS 326: Intro Cmptr Vision Pttrn Rcgtn
CS 490: Ind Study in Computer Science
CS 631: Topic: Computer Vision
CS 671: Computer Vision
CS 693: Thesis I
CS 694: Thesis II
CS 702: Research Seminar
CS 740: Advanced Computer Vision
CS 802: Research Seminar
CS 804: Independent Research
CS 806: Dissertation Preparation
CS 840: Advanced Computer Vision
CS 896: Computer Vision
DCS 861: Topic:Digital Image Processing
DCS 891: Research Seminar VI
DCS 990: Dissertation for DPS in Cmptng
DCS 991: Dssrttn for DPS in Cmptg II
IS 395: Independent Study in IS
MM 803: Maintain Matriculation-CS PhD

Publications and Presentations

Publications

Automatic Measurement of Joint Space Width from Hand Radiographs using Deep Learning Models
Chavda, H., Shan, J. & Zhang, M. (2024).

Improving knee osteoarthritis classification using multimodal intermediate fusion of X-ray, MRI, and clinical information
Guida, C., Shan, J. & Zhang, M. (2023). Neural Computing and Applications.

Finger Joint Segmentation Using Machine Learning and Minimized Training Set
Wang, Y., , M. Z., Cheung, T., Guida, C., Ren, R. & Shan, J.

Automated Joint Space Width Measurement for Hand Osteoarthritis: A Deep Learning Approach
Chang, Z., Shan, J., Driban, J., McAlindon, T., Duryea, J., Schaefer, L., Eaton, C. & Zhang, M.

Finger Joint Segmentation Using Machine Learning and Minimized Training Set
Wang, Y., Zhang, M., Cheung, T., Guida, C., Ren, R. & Shan, J. (2022).

Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI
Almajalid, R., Zhang, M. & Shan, J. (2022). Diagnostics. Vol 12 (Issue 1) , pages 123.

Automated Hand Osteoarthritis Classification Using Convolutional Neural Networks
Guida, C., Blackadar, J., Yang, Z., Driban, J., Duryea, J., Scheafer, L., Eaton, C., McAlindon, T. & Shan, J. (2021).

Automatic Hand Segmentation from Hand X-rays Using Minimized Training Samples and Machine Learning Models
Yang, Z., Shan, J., Guida, C., Blackadar, J., Cheung, T., Driban, J., McAlindon, T. & Zhang, M. (2021).

Automatic Hand Segmentation from Hand X-rays Using Minimized Training Samples and Machine Learning Models
Yang, Z., Guida, C., Shan, J., Blackadar, J., Cheung, T., Driban, J., McAlindon, T. & Zhang, M. (2021).

Bone Marrow Lesion Segmentation Using Synthetic Data and Deep Learning Models
Michaely, B., Zhang, M. & Shan, J. (2021).

Knee Osteoarthritis Classification Using 3D CNN and MRI
Guida, C., Zhang, M. & Shan, J. (2021). Applied Sciences. Vol 11 (Issue 11) https://www.mdpi.com/2076-3417/11/11/5196

Patients’ perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data
Zhang, Z., Citardi, D., Wang, D., Genc, Y., Shan, J. & Fan, X. (2021). Health Informatics Journal. Vol 27 (Issue 2)

Automated cell division classification in early mouse and human embryos using convolutional neural networks
Malmsten, J., Zaninovic, N., Zhan, Q., Rosenwaks, Z. & Shan, J. (2020). Neural Computing and Applications.

Knee Bone Segmentation on Three-Dimensional MRI
Almajalid, R., Shan, J., Zhang, M., Stonis, G. & Zhang, M. (2019).

Identification of Knee Cartilage Changing Pattern
Almajalid, R., Shan, J. & Du, Y. (2019). Applied Sciences. Vol 9 (Issue 17) , pages 3469. https://www.mdpi.com/2076-3417/9/17/3469

Automated cell stage predictions in early mouse and human embryos using convolutional neural networks
Malmsten, J. & Shan, J. (2019).

Bone Segmentation in 3D Knee MRI Images Using U-Net
Alon, T., Shan, J., Zhang, M., Delvecchio, J. & Zhang, M. (2019).

Convolutional Neural Networks for Breast Ultrasound Image Segmentation
Liang, Y., Shan, J., Benjamin, D. & Almajalid, R. (2019).

A New Scheme to Evaluate the Accuracy of Knowledge Representation in Automated Breast Cancer Diagnosis
Shan, J. (2014).

Professional Contributions and Service

Professional Memberships

  • IEEE membership
  • IEEE Women in Engineering

Department Service

  • Computer Science Curriculum Committee [Committee Chair]