Early lifespan prediction in Caenorhabditis elegans via contrastive learning and channel attention

Early lifespan prediction in Caenorhabditis elegans faces the challenges of indistinct discriminative signals, subtle and localized key features, difficulty in data annotation, and poor generalization. We propose Contrastive Learning-guided Channel Attention Modulation (CLCAM), in which supervised contrastive learning clusters individuals with the same lifespan and separates different classes. The resulting embedding drives channel-wise gains that are additively coupled to the backbone, thereby amplifying subtle morphological cues. At inference, the contrastive branch is removed, keeping FLOPs essentially unchanged with a modest runtime cost on our hardware. On a public dataset, CLCAM achieves an AUC-ROC of 0.84, showing a consistent improvement over the EfficientNet-B3 baseline (0.82) and a substantial gain over the prior WormNet model (0.61). Grad-CAM indicates attention focused on the pharynx and body-wall musculature, supporting the biological plausibility of the model's decisions. CLCAM offers a clear, low-overhead paradigm for early lifespan phenotyping. CLCAM code is available at https://github.com/JMM502/CLCAM/tree/master/clcam.