Selected Publications

Find all articles, including manuscript (pre-print), on my Google Scholar profile.

Journal Papers

• Rachmadi, M.F., Hernández, M.V., Li, H., Guerrero, R., Meijboom, R., Wiseman, S., Waldman, A., Zhang, J., Rueckert, D., Wardlaw, J. and Komura, T., (2020). “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”. In Computerized Medical Imaging and Graphics, 79, 101685. doi: doi.org/10.1016/j.compmedimag.2019.101685.
• Jeong Y., Rachmadi M.F., Valdés Hernández M.C., and Komura T. (2019). “Dilated Saliency U-Net for White Matter Hyperintensities Segmentation using Irregularity Age Map”. Accpeted in Front. Aging Neuroscience. pre-print doi: 10.1101/550517
• Pérez Malla C.U., Valdés Hernández M.C., Rachmadi M.F., and Komura T. (2019). “Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images Using a Convolutional Neural Network Scheme”. Front. Neuroinform. 13:33. doi: 10.3389/fninf.2019.00033
• Rachmadi, M. F., Valdés-Hernández, M. D. C., Agan, M. L. F., Di Perri, C., Komura, T., & Alzheimer’s Disease Neuroimaging Initiative. (2018). “Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology”. Computerized Medical Imaging and Graphics, 66, 28-43. doi: 10.1016/j.compmedimag.2018.02.002
• Rachmadi, M.F., Valdés-Hernández, M., Agan, M., & Komura, T. (2017). “Deep learning vs. conventional machine learning: pilot study of WMH segmentation in brain MRI with absence or mild vascular pathology”. Journal of Imaging, 3(4), 66. doi: 10.3390/jimaging3040066
• Syulistyo, A. R., Purnomo, D. M. J., Rachmadi, M. F., & Wibowo, A. (2016). “Particle swarm optimization (PSO) for training optimization on convolutional neural network (CNN)”. Jurnal Ilmu Komputer dan Informasi, 9(1), 52-58.
• Rachmadi, M. F., Rustamadji, R., Purwanegara, M. K., & Hardjono, B. (2014). “Peer Assessment Rating (PAR) Index calculation on 2D dental model image for over jet, open bite, and teeth segmentation on occlusion surface”. Jurnal Ilmu Komputer dan Informasi, 7(1), 44-53.

Conference Papers

• Rachmadi, M. F., Valdés-Hernández, M. D. C., Makin, S., Wardlaw, J. M., & Komura, T. (2019). “Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map”. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11766. Springer, Cham. doi: doi.org/10.1007/978-3-030-32248-9_17
• Rachmadi, M. F., Valdés-Hernández, M. D. C., & Komura, T. (2018, September). “Automatic irregular texture detection in brain mri without human supervision”. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2018 - MICCAI 2018 (pp. 506-513). Springer, Cham. doi: 10.1007/978-3-030-00931-1_58
• Rachmadi, M. F., Valdés-Hernández, M. D. C., & Komura, T. (2018, September). “Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression using Irregularity Age Map in Brain MRI”. In International Workshop on PRedictive Intelligence In MEdicine (pp. 85-93). Springer, Cham. doi: 10.1007/978-3-030-00320-3_11
• Rachmadi, M. F., Valdés-Hernández, M. D. C., & Komura, T. (2017, October). “Voxel-based irregularity age map (IAM) for brain’s white matter hyperintensities in MRI”. In 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 321-326). IEEE. doi: 10.1109/ICACSIS.2017.8355053
• Rachmadi, M. F., Valdés-Hernández, M. D. C., Agan, M. L. F., Komura, T., & Alzheimer’s Disease Neuroimaging Initiative. (2017, July). “Evaluation of Four Supervised Learning Schemes in White Matter Hyperintensities Segmentation in Absence or Mild Presence of Vascular Pathology.” In Annual Conference on Medical Image Understanding and Analysis (pp. 482-493). Springer, Cham. doi: 10.1007/978-3-319-60964-5_42
• Rakun, E., Rachmadi, M. F., & Danniswara, K. (2012, December). “Spectral domain cross correlation function and generalized learning vector quantization for recognizing and classifying indonesian sign language”. In 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 213-218). IEEE.
• Hardjono, B., Wibowo, A., Rachmadi, M. F., & Jatmiko, W. (2012, November). “Mobile phones as traffic sensors with map matching and privacy considerations”. In 2012 International Symposium on Micro-NanoMechatronics and Human Science (MHS) (pp. 450-455). IEEE.
• Rachmadi, M. F., Ma’sum, M. A., Setiawan, I. M. A., & Jatmiko, W. (2012, August). “Fuzzy learning vector quantization particle swarm optimization (FLVQ-PSO) and fuzzy neuro generalized learning vector quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram”. In 2012 Proceedings of SICE Annual Conference (SICE) (pp. 465-470). IEEE.
• Rachmadi, M. F., Al Afif, F., Jatmiko, W., Mursanto, P., Manggala, E. A., Ma’sum, M. A., & Wibowo, A. (2011, November). “Adaptive traffic signal control system using camera sensor and embedded system”. In TENCON 2011-2011 IEEE Region 10 Conference (pp. 1261-1265). IEEE.
• Al Afif, F., Rachmadi, M. F., Wibowo, A., Jatmiko, W., Mursanto, P., & Ma’sum, M. A. (2011, November). “Enhanced adaptive traffic signal control system using camera sensor and embedded system”. In 2011 International Symposium on Micro-NanoMechatronics and Human Science (pp. 367-372). IEEE.