Ten research papers by IIT Armour College of Engineering students, under the guidance of Dr. Suzuki, Associate Professor of Electrical and Computer Engineering, were accepted and presented at the Radiological Society of North America (RSNA) conference. The RSNA is known not only 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.

PhD 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).

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; J V Patel, BS; J Liu, MS; P Forti; K Suzuki, PhD

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, MS, A Zarshenas, MS, Z Wei, BS, L Yang, MD, PhD, L Fajardo, MD, MBA, K Suzuki, PhD

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, MS; J Liu, MS; K Suzuki, PhD

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, MS; J Liu, MS; K Suzuki, PhD

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; K Suzuki, PhD; T Higaki, PhD; Y Zhao, BS; A Zarshenas, MS; 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, MS; J Liu, MS; K Suzuki, PhD

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, MS; Y Zhao, BS; J Liu, MS; T Higaki, PhD; K Awai, MD; K Suzuki, PhD

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: J Liu, MS, A Zarshenas, MS, Z Wei, BS, L Yang, MD, PhD, L Fajardo, MD, MBA, K Suzuki, PhD

Title: What Was Changed in Machine Learning (ML) in Medical Image Analysis After the Introduction of Deep Learning? Authors: K Suzuki, PhD; A Zarshenas, MS; J Liu, MS; Y Zhao, BS; Y Luo

Title: How Deep Should We Go with Deep Learning in Medical Image Analysis? Authors: N Tajbakhsh; A Zarshenas, MS; J Liu, MS; K Suzuki, PhD