Chronological Contrastive Learning: Few-Shot Progression Assessment in Irreversible Diseases

Clemens Watzenböck, Daniel Aletaha, Michael Deman, Thomas Deimel, Jana Eder, Ivana Janickova, Robert Janiczek, Peter Mandl, Philipp Seeböck, Gabriela Maria Supp, Paul Weiser, Georg Langs

Keywords: Unsupervised Learning, Contrastive Learning, Few-Shot Learning, Representation Learning, Longitudinal Medical Imaging, Disease Progression, Rheumatoid Arthritis

TL;DR: We introduce CHRONOCON, an unsupervised contrastive loss that learns progression features directly from the chronological order of scans, tailored to irreversible processes.

Abstract:

Quantitative disease severity scoring in medical imaging is costly, time-consuming, and subject to inter-reader variability. At the same time, clinical archives contain far more longitudinal imaging data than expert-annotated severity scores. Existing self-supervised methods typically ignore this chronological structure.
We introduce ChronoCon,
a contrastive learning approach that replaces label-based ranking losses with rankings derived solely from the visitation order of a patient’s longitudinal scans. Under the clinically plausible assumption of monotonic progression in irreversible diseases, the method learns disease-relevant representations without using any expert labels. This generalizes the idea of Rank-N-Contrast from label distances to temporal ordering.
Evaluated on rheumatoid arthritis radiographs for severity assessment, the learned representations substantially improve label efficiency. In low-label settings, ChronoCon significantly
outperforms a fully supervised baseline initialized from ImageNet weights.
In a few-shot learning experiment, fine-tuning ChronoCon on expert scores from only five patients yields an intraclass correlation coefficient of 86% for severity score prediction.
These results demonstrate the potential of chronological contrastive learning to exploit routinely available imaging metadata to reduce annotation requirements in the irreversible disease domain.

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Learning with Noisy Labels and Limited Data

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Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 241