Keywords: Renal transplantation, contrastive learning, MRI, representation learning
TL;DR: In this work, we propose contrastive learning schemes based on 3D CNN to generate meaningful representations of DCE MRI for kidney transplants associated with different relevant clinical information.
Abstract: In this work, we propose contrastive learning schemes based on a 3D Convolutional Neural Network (CNN) to generate meaningful representations for kidney transplants associated with different relevant clinical information. To deal with the problem of a limited amount of data, we investigate various two-stream schemes pre-trained in a contrastive manner, where we use the cosine embedding loss to learn to discriminate pairs of inputs. Our universal 3D CNN models identify low dimensional manifolds for representing Dynamic Contrast-Enhanced Magnetic Resonance Imaging series from four different follow-up exams after the transplant surgery. Feature visualization analysis highlights the relevance of our proposed contrastive pre-trainings and therefore their significance in the study of chronic dysfunction mechanisms in renal transplantation, setting the path for future research in this area. The code is available at https://github.com/leomlck/renal_transplant_imaging.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Unsupervised Learning and Representation Learning
Secondary Subject Area: Application: Radiology
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Code And Data: https://github.com/leomlck/renal_transplant_imaging