Contrastive Masked Transformers for Forecasting Renal Transplant Function - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

Contrastive Masked Transformers for Forecasting Renal Transplant Function

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 paper, we propose a sequential architecture based on transformer encoders to predict the renal function 2-years post-transplantation. Our method uses features generated from Dynamic Contrast-Enhanced Magnetic Resonance Imaging from 4 follow-ups during the first year after the transplant surgery. To deal with missing data, a key mask tensor exploiting the dot product attention mechanism of the transformers is used. Moreover, different contrastive schemes based on cosine similarity distance are proposed to handle the limited amount of available data. Trained on 69 subjects, our best model achieves 96.3% F1 score and 98.9% ROC AUC in the prediction of serum creatinine threshold on a separated test set of 20 subjects. Thus, our experiments 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. Our code is available at https://github.com/leomlck/renal_transplant_imaging.
Fichier principal
Vignette du fichier
paper1582.pdf (1.67 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03738395 , version 1 (26-07-2022)

Identifiers

  • HAL Id : hal-03738395 , version 1

Cite

Leo Milecki, Vicky Kalogeiton, Sylvain Bodard, Dany Anglicheau, Jean-Michel Correas, et al.. Contrastive Masked Transformers for Forecasting Renal Transplant Function. MICCAI 2022 - 25th International Confernce on Medical Image Computing and Computer Assisted Intervention, Sep 2022, Singapore, Singapore. pp 244-254. ⟨hal-03738395⟩
187 View
315 Download

Share

Gmail Mastodon Facebook X LinkedIn More