Febrian Rachmadi

Febrian Rachmadi

Faculty Member, Faculty of Computer Science, Universitas Indonesia // Research Scientist, RIKEN Center for Brain Science

About me

I am a faculty member of the Faculty of Computer Science, Universitas Indonesia. I am also affiliated as a postdoctoral research scientist at Brain Image Analysis (BIA) Unit, RIKEN Center of Brain Science (RIKEN CBS), Wako, Japan working together with Dr. Henrik Skibbe, the team leader of BIA Unit to develop machine/deep learning techniques for processing and analyzing marmoset brain image data. My main area of interest is medical image analysis and computation using data-driven methods, such as deep learning algorithms.

I did my PhD and MSc studies at the School of Informatics, University of Edinburgh, supervised by Prof. Taku Komura and co-supervised by Dr. Maria Valdés Hernández. At that time, I was affiliated with the Computer Graphics and Visualisation (CGVU) research group in the Institute of Perception, Action and Behaviour (IPAB) (School of Informatics) and Centre for Clinical Brain Sciences (CCBS). My PhD thesis is titled “Development of machine learning schemes for segmentation, characterisation, and evolution prediciton of white matter hyperintensities in structural brain MRI” and examined by Prof. Emanuale Trucco from the University of Dundee (external examiner) and Dr. Mohsen Khadem from the University of Edinburgh (internal examiner).

Previously, I worked closely with Prof. Wisnu Jatmiko at the Faculty of Computer Science, Universitas Indonesia. Some of my previous works are automatic vehicle counting system for intelligent traffic system, automatic peer assessment rating (PAR) index calculation on dental image. Please see list of my reserach/project and publications in the corresponding pages.

Working contact point: febrian (dot) rachmadi (at) riken (dot) jp [without spaces]

News (Updated on 14/04/2020)

  • 01/09/2020: Joined the Faculty of Computer Science, Universitas Indonesia as a researcher.
  • 01/03/2020: Started my postdoctoral research scientist position at the Brain Image Analysis Unit, RIKEN Center for Brain Science, Japan.
  • 06/02/2020: Successfully defended my PhD thesis titled “Development of machine learning schemes for segmentation, characterisation, and evolution prediciton of white matter hyperintensities in structural brain MRI”! [PDF]
  • 13/11/2019: A journal paper titled “Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images” has been published in Computerized Medical Imaging and Graphics! [full-paper] [pre-print]
  • 01/11/2019: A new manuscript titled “Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks” is now online! [pre-print]
  • 17/10/2019: A new blog entry discussing MICCAI 2019 papers on predicting the progression of disease!
  • 06/06/2019: A conference paper titled “Dilated Saliency U-Net for White Matter Hyperintensities Segmentation using Irregularity Age Map” has been published in Frontiers in Aging Neuroscience! [full-paper]
  • 05/06/2019: A journal paper titled “Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map” has been accepted at MICCAI 2019! [full-paper] [pre-print]
  • 04/06/2019: The site is now online!