Ten Armour College of Engineering ECE Papers Accepted by the Radiology Society of America

Ten research papers by Illinois Tech Armour College of Engineering students, under the guidance of Associate Professor of Electrical and Computer Engineering Kenji Suzuki, were accepted and presented at the Radiological Society of North America (RSNA) conference. The RSNA is known as the biggest clinical international conference in medicine with the largest number of participants (more than 60,000).

Electrical and Computer Engineering (ECE) Ph.D. students include, Junchi Liu, Amin Zarshenas, Paul Forti, and Yuji Zhao. ECE graduate students include Zheng Wei and Jaimeet Patel.

“I am very proud of my students for their great accomplishments, and because it is generally difficult even for doctors and professors to get papers accepted for presentation at this prestigious conference,” explained Suzuki.

Ph.D. students are funded by Suzuki’s external grants and TA/RA from the ECE department. Master students participate in Suzuki’s research project course, Special Problems in Electrical and Computer Engineering (ECE-597).

View the conference program here.

The research papers are listed below:

  • Title: Virtual Dual-Energy (VDE) Imaging: Separation of Bones from Soft Tissue in Chest Radiographs (CXRs) by Means of Anatomy-Specific (AS) Orientation-Frequency-Specific (OFS) Deep Neural Network Convolution (NNC) Authors: A Zarshenas, MSc, Chicago, IL; J V Patel, BS; J Liu, MS; P Forti; K Suzuki, Ph.D.
  • Title: Virtual High-Dose (VHD) Technology: Radiation Dose Reduction in Digital Breast Tomosynthesis (DBT) by Means of Supervised Deep-Learning Image Processing (DLIP) Authors: Junchi Liu, M.S., Amin Zarshenas, M.S., Zheng Wei, B.S., Limin Yang, M.D., Ph.D., Laurie Fajardo, M.D.,M.B.A., Kenji Suzuki, Ph.D.
  • Title: Investigating the Depth of Convolutional Neural Networks (CNNs) in Computer-aided Detection and Classification of Focal Lesions: Lung Nodules in Thoracic CT and Colorectal Polyps in CT Colonography Authors: N Tajbakhsh; A Zarshenas, MSc; J Liu, MS; K Suzuki, Ph.D.
  • Title: Two Deep-Learning Models for Lung Nodule Detection and Classification in CT: Convolutional Neural Network (CNN) vs Neural Network Convolution (NNC) Authors: N Tajbakhsh; A Zarshenas, MSc; J Liu, MS; K Suzuki, Ph.D.
  • Title: Detection of Solid Pulmonary Nodules in Micro-Dose CT (mDCT) with “Virtual” Higher-Dose (vHD) CT Technology: An Observer Performance Study Authors: W Fukumoto, Hiroshima, Japan; K Suzuki, Ph.D.; T Higaki, Ph.D.; Y Zhao, BSC; A Zarshenas, MSc; K Awai.
  • Title: Highly Efficient Biomarker Selection (BS) Based on Novel Binary Coordinate Accent (BCA) for Machine Learning with a Large Dataset in Radiomics Authors: A Zarshenas, MSc, Chicago, IL; J Liu, MS; K Suzuki, Ph.D.
  • Title: Radiation Dose Reduction in Thin-Slice Chest CT at a Micro-Dose (mD) Level by Means of 3D Deep Neural Network Convolution (NNC) Authors: A Zarshenas, MSc, Chicago, IL; Y Zhao, BSC; J Liu, MS; T Higaki, Ph.D.; K Awai, MD; K Suzuki, Ph.D.
  • Title: Computer-Based Interactive Demonstration and Comparative Study: Virtual Full-Dose (VFD) Digital Breast Tomosynthesis (DBT) Images Derived From Reduced-Dose Acquisitions versus Clinical Full-Dose DBT Images Authors: Junchi Liu, M.S., Amin Zarshenas, M.S., Zheng Wei, B.S., Limin Yang, M.D., Ph.D., Laurie Fajardo, M.D.,M.B.A., Kenji Suzuki, Ph.D.
  • Title: What Was Changed in Machine Learning (ML) in Medical Image Analysis After the Introduction of Deep Learning? Authors: K Suzuki, Ph.D.; A Zarshenas, MSc; J Liu, MS; Y Zhao, BSC; Y Luo
  • Title: How Deep Should We Go with Deep Learning in Medical Image Analysis? Authors: N Tajbakhsh; A Zarshenas, MSc; J Liu, MS; K Suzuki, Ph.D.