Data-driven methods for renal transplantation monitoring
LIVIA seminar
Speaker: L茅o Milecki, PhD candidate at CentraleSupelec, Paris-Saclay University, France
Abstract:
Renal transplantation appears as the most effective solution for end-stage renal disease. However, it may lead to renal allograft rejection or dysfunction within 15%-27% of patients in the first 5 years post-transplantation. Resulting from a simple blood test, serum creatinine is the primary clinical indicator of kidney function by calculating the Glomerular Filtration Rate. These characteristics motivate the challenging task of predicting serum creatinine early post-transplantation while investigating and exploring its correlation with imaging data. In this talk, I will present our recent work regarding this task, which exploits transformer encoders and contrastive learning schemes. Our experiments aim to highlight the relevance of considering sequential imaging data for this task and therefore in the study of chronic dysfunction mechanisms in renal transplantation, setting the path for future research in this area.
Short bio:
L茅o Milecki is a PhD student at MICS, CentraleSupelec, Paris-Saclay University, France (near Paris) under the supervision of Maria Vakalopoulou at MICS and Marc-Olivier Timsit, Paris University. His PhD thesis focuses on applying novel Machine Learning algorithms to analyze biomedical data toward graft rejection diagnostic or prognosis after renal transplantation, focusing on Deep Learning and un-/weakly-/self-supervised methods. He is currently visiting Provost Ultrasound Lab at Polytechnique Montreal until 21st December.
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