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Seokbum Ko

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Department Head and Professor

University of Saskatchewan

Canada

Research Interests

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Professors in Canada Professors in Artificial Intelligence Artificial Intelligence professors in Canada

About

Seokbum Ko is a Professor at the University of Saskatchewan, Canada. His research interests encompass advanced computational models and artificial intelligence applications in medical imaging, as evidenced by his recent articles on topics such as detecting schizophrenia and diagnosing rib fractures. Additionally, he explores interdisciplinary approaches including deep learning for agricultural yield prediction and novel architectural designs in technology.

Recent Grants

Grant: Close

High performance and energy efficient deep learning processor design

Open Date: 2018-04-01

Close Date: 2019-03-31

Grant: Close

Next generation residential kitchen fire prevention system

Open Date: 2016-04-01

Close Date: 2017-03-31

Grant: Close

A study on decimal floating-point arithmetic unit through fused multiply-add architecture

Open Date: 2012-04-01

Close Date: 2017-03-31

Grant: Close

Biological sensor technology for safety and security

Open Date: 2012-04-01

Close Date: 2014-03-31

Grant: Close

FPGA based image processing system using network on chip

Open Date: 2012-04-01

Close Date: 2013-03-31

Articles (30)

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2022

Collaborators (6)

Hyun Kim

Associate Professor

Seoul National University

SOUTH KOREA

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Gong Yong Jin

SOUTH KOREA

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Eun Hae Park

SOUTH KOREA

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Raymond Spiteri

Professor

University of Saskatchewan

CANADA

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Alexis Carter

Adjunct Associate Professor

Emory University

UNITED STATES

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Riel Castro-Zunti

University of Saskatchewan

CANADA

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Seokbum Ko

Research Interests

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About

Recent Grants

Articles (30)

An Introduction to AI for Clinicians: Tutorial

FLASH: Energy-Efficient FPGA Acceleration via Linear Approximation and Streamlined Two-Stage Pipeline Architectures for Quantized CNN--Transformer Hybrid Networks

An accurate, straightforward computer vision algorithm for optimal tumor-feeding visualization in cone-beam computed tomography hepatic arteriography: A preliminary study

FFT-based deep learning for efficient combustion instability prediction: A comparative study of time and frequency-domain approaches

An Optimized Stochastic Computing Architecture With Reduced Correlation Induced Errors

FlexPWL: A Flexible, Scalable, and Multiplier-Free Approach for Activation Functions on FPGA

LYRA: Low-Frequency Rank Adaptation via Factored DCT Coefficients for Parameter Efficient Fine Tuning of Transformers

T3: Transformer Accelerator With Efficient Top-K Sorting and Dynamic Tanh Computation for Marine Edge Computing

The multi-GPU Wetland DEM Ponding Model

Reliable Synchronous and Asynchronous Counter Design in QCA

Diagnosing Ankylosing Spondylitis via Architecture-Modified ResNet and Combined Conventional Magnetic Resonance Imagery

Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images

Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images

LightIDS: a lightweight neural network-based intrusion detection system

A Novel Vision Transformer + InceptionV3 Hybrid Network for Accurate Diagnosis of Ankylosing Spondylitis from Computed Tomography Scans

DECTGoutSys: Reducing False Positive Gout Diagnoses via a Machine Vision Pipeline for Crystal Tophi Identification+Classification in Dual-Energy Computed Tomography (DECT)

Diagnosis of Rib Fracture Using Artificial Intelligence on Chest CT Images of Patients with Chest Trauma

RibFractureSys: A gem in the face of acute rib fracture diagnoses

Decoder Reduction Approximation Scheme for Booth Multipliers

A Novel Optimized Deep Learning Model for Canola Crop Yield Prediction on Edge Devices

PaXNet: Tooth segmentation and dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier

Booth Encoding-Based Energy Efficient Multipliers for Deep Learning Systems

Interpretability of artificial intelligence models that use data fusion to predict yield in aeroponics

Efficient Approximate Posit Multipliers for Deep Learning Computation

Non-Bianisotropic Complementary Split Ring Resonator Metamaterial Bandstop Filter Using Cylindrical Metal Vias

A Long Short-Term Memory-Based Interconnected Architecture for Classification of Grasp Types Using Surface-Electromyography Signals

An Optimized Hardware Implementation of Modular Multiplication of Binary Ring LWE

Asymmetric meshed ground Balanced Antipodal Vivaldi Antenna with quarter-wavelength labyrinth director for ultrawideband microwave applications

Histopathological carcinoma classification using parallel, cross-concatenated and grouped convolutions deep neural network

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

Collaborators (6)

Hyun Kim

Gong Yong Jin

Eun Hae Park

Raymond Spiteri

Alexis Carter

Riel Castro-Zunti

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