Professional background

PhD from Western Norway University of Applied Sciences, Department of Computer science, Electrical engineering and Mathematical sciences My research activities primarily revolved around machine learning and medical image analysis, with a particular focus on design methodologies in deep learning for efficient use of data.

Currently, I am employed as a Data Scientist at Lerøy Seafood, a global leader in the seafood industry.My primary responsibilities include implementing MLOps and incorporating large language models into our operational strategies, aiming to improve business efficiency and decision-making.

Publications

fastMONAI: a low-code deep learning library for medical image analysis
S. Kaliyugarasan, A.S Lundervold., Software Impacts, 2023

Multi-Center CNN-based spine segmentation from T2w MRI using small amounts of data
S. Kaliyugarasan, A.S Lundervold and others., 20th IEEE International Symposium on Biomedical Imaging (ISBI)

LAB-Net: Lidar and aerial image-based building segmentation using U-Nets
S. Kaliyugarasan, A.S Lundervold., Nordic Machine Intelligence, second place 2022 NORA MapAI competition

Fully automatic whole-volume tumor segmentation in cervical cancer
E. Hodneland, S. Kaliyugarasan, and others., Cancers, 2022

Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI
S. Kaliyugarasan, A. Lundervold, A.S Lundervold., IJIMAI, 2021

2D and 3D U-Nets for skull stripping in a large and heterogeneous set of head MRI using fastai
S. Kaliyugarasan, M. Kocinski, A. Lundervold, A.S Lundervold., NIK2020, no.1, 2020

%# Preprints, submitted and in preparation

Conference posters

fastMONAI: a low-code deep learning library for medical image analysis
with A.S Lundervold., poster at MMIV Conference 2022, Bergen, Norway, Dec. 2022

Brain age versus chronological age: A large scale MRI and deep learning investigation
with A. Lundervold and A.S Lundervold., EPOS scientific poster at ECR 2020, online, Jul. 2020

Transfer learning for medical images: a case study
with A.S Lundervold., Poster at GTC Europe 2018, Munich, Germany, Oct. 2018

Teaching

Past courses

• DAT158: Machine learning engineering and advanced algorithms., Fall 2021 . Course website: DAT158-ML-21

Completed MSc projects (co-supervision)

• Jostein Digernes and Carsten Ditlev-Simonsen (2020–2022): A workflow-integrated brain tumor segmentation system based on fastai and MONAI

Talks and travels

Latest and upcoming

Past events

• RAD230 lecture and lab. Title: Kunstig intelligens og framtidens radiografer, Western Norway University of Applied Sciences, Bergen, Norway, May 8, 2023
• ISBI 2023. Title: Multi-Center CNN-based spine segmentation from T2w MRI using small amounts of data., Cartagena de Indias, Colombia, April 18-21, 2023
• Bouvet deler. Title: Kunstig intelligens i medisinsk bildebehandling., Litteraturhuset i Bergen, Norway, January 19, 2023
• RSNA 2022. Title: Fully automatic whole-volume tumor segmentation in cervical cancer., McCormick Place, Chicago, IL, USA, December 27 - December 1, 2022
• RAD230 lecture and lab. Title: Hva er dyplæring?, Western Norway University of Applied Sciences, Bergen, Norway, May 13, 2022
• DAT255 guest lecture. Title: How to use fastai for medical image analysis., Western Norway University of Applied Sciences, Bergen, Norway, March 2, 2022
• CurieLecture. Title: Praktisk dyp læring i medisinsk bildeanalyse., Eitri Medical Incubator, Bergen, Norway, February 2, 2022
• NordicAIMeet 2021 – Nordic Young Researchers Symposium. Title: How old is your brain? A Large Scale MRI and Deep Learning Investigation., Oslo Kongressenter, Norway, November 1-2, 2021
• Seminar in Engineering Computing. Title: Practical deep learning in medical image analysis., Western Norway University of Applied Sciences, Bergen, Norway, May 21, 2021
• Medisinsk bildebehandling og maskinlæring. Title: Kunstig intelligens ved MMIV, Radiologisk avdeling, Haukeland universitetssykehus., Streaming, October 12, 2020
• Seminar, Bergen gynekologisk kreft - Voss 2020. Title: Artificial intelligence in image diagnostics: design methodologies for efficient use of data and radiologist’s expertise., Scandic Voss, Norway, March 5-6, 2020
• Bergen Data Science Meetup. Title: Artificial intelligence in image diagnostics: design methodologies for efficient use of data., Bouvet ASA, Bergen, Norway, November 19, 2019
• TekPRAT Førde - Maskinlæring og kunstig intelligens. Title: MMIV@HUS: Kunstig intelligens ved radiologisk avdeling., Førde Sentralsjukehus, Norway, September 23, 2019
• MMIV Seminar September 2019. Title: Artificial intelligence in image diagnostics – transfer learning and active learning for efficient use of data and radiologist’s expertise., Haraldsplass Diakonale Sykehus, Bergen, Norway, September 20, 2019
• NordBioMedNet Summer School 2019 in Computational Biomedicine - Imaging, machine learningn and precision medicine. Title: Deep Learning in medical image analysis., Seili, Finland, August 11-16, 2019
• Exhibition at Christiekonferansen 2019. Title: Mohn Medical Imaging and Visualization Center.,   Universitetsaulaen i Bergen, Norway, April 29, 2019
• Bergen AI & Machine Learning Symposium 2019. Title: Deep transfer learning: a case study., Solstrand Hotel, Bergen, Norway, March 25-26, 2019
• Exhibition at EHiN 2018., Oslo Spektrum, Norway, November 14, 2018
• Machine learning seminar: A machine learning mini-conference. Title: Deep transfer learning: Can a network trained to do a task be reused for other tasks?, Haukeland universitetssjukehus, Bergen, Norway, October 17, 2018
• Poster at GTC Europe 2018. Title: Transfer learning for medical images: a case study., Munich, Germany, October 9-11, 2018

Additional activites

• Partner, AkademiX
• Co-hosting Bergen Data Science meetup

Master thesis

Deep transfer learning in medical imaging
S. Kaliyugarasan, University of Bergen and Western Norway University of Applied Sciences, 2019