This repository contains supplementary code for Time and Event-Aware Self-Supervised Learning for Alzheimer's Disease Progression Analysis, published in The Medical Image Computing and Computer Assisted Intervention Society (MICCAI) 2024. In this work, we analyze the effect of incorporating time and event labels to a self-supervised pretraining pipeline for survival analysis of Alzheimer's Dementia.
Figure: Schematic diagram of the proposed time- and event-aware SSL, where
Usage:
from lib.Loss import TESSL_Loss
# alpha and beta define maximum and minimum weight
loss_fn = TESSL_loss(alpha=1, beta=0.5)
# features in [batch_size, n_views, dim]
# events, times in [batch_size]
tessl_loss = loss_fn(features, labels=events, times=times)
# Compute SupCon by excluding times
supcon_loss = loss_fn(features, labels=events, times=None)
# Compute SimCLR by exluding events and times
simclr_loss = loss_fn(features, labels=None, times=None)Our data consists of a cohort of 493 unique patients from the ADNI dataset (link). Specific data splits can be found in datasets/files folder. Images were preprocessed via Clinica according to process define in "Generalizable deep learning model for early Alzheimer's Disease detection from structural MRIs". Refer to here for more details
Comparison against regular SSL, Event-Aware SSL and No Pretaining baseline. Results averaged across 3 seeds
| Method | C-td | IBS |
|---|---|---|
| No Pretraining | 0.7329 | 0.2099 |
| SSL | 0.7511 | 0.1985 |
| E-SSL | 0.7720 | 0.1997 |
| TE-SSL | 0.7873 | 0.1889 |
t-SNE visualization demonstrates superior seperability for TE-SSL embeddings compared to SSL and E-SSL.
@InProceedings{10.1007/978-3-031-72390-2_31,
author="Thrasher, Jacob
and Devkota, Alina
and Tafti, Ahmad P.
and Bhattarai, Binod
and Gyawali, Prashnna",
editor="Linguraru, Marius George
and Dou, Qi
and Feragen, Aasa
and Giannarou, Stamatia
and Glocker, Ben
and Lekadir, Karim
and Schnabel, Julia A.",
title="TE-SSL: Time and Event-Aware Self Supervised Learning for Alzheimer's Disease Progression Analysis",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="324--333",
abstract="Alzheimer's Disease (AD) represents one of the most pressing challenges in the field of neurodegenerative disorders, with its progression analysis being crucial for understanding disease dynamics and developing targeted interventions. Recent advancements in deep learning and various representation learning strategies, including self-supervised learning (SSL), have shown significant promise in enhancing medical image analysis, providing innovative ways to extract meaningful patterns from complex data. Notably, the computer vision literature has demonstrated that incorporating supervisory signals into SSL can further augment model performance by guiding the learning process with additional relevant information. However, the application of such supervisory signals in the context of disease progression analysis remains largely unexplored. This gap is particularly pronounced given the inherent challenges of incorporating both event and time-to-event information into the learning paradigm. Addressing this, we propose a novel framework, Time and Event-aware SSL (TE-SSL), which integrates time-to-event and event and data as supervisory signals to refine the learning process. Our comparative analysis with existing SSL-based methods in the downstream task of survival analysis shows superior performance across standard metrics. The full code can be found here: https://github.com/jacob-thrasher/TE-SSL.",
isbn="978-3-031-72390-2"
}