CADS: Conformal Adaptive Decision System for Cost-Efficient Image Classification
For practitioners deploying image classification models, CADS offers a cost-efficient alternative to one-size-fits-all heavy models, particularly in resource-constrained settings like clinical diagnostics.
CADS introduces a sequential multi-model algorithm that uses conformal prediction to dynamically route images through a cascade of models based on estimated complexity, achieving up to 12x lower computational cost while maintaining high accuracy on two datasets.
While high-capacity AI models have advanced state-of-the-art performance, their practical deployment is often hindered by high inference costs, environmental impact, and a "one-size-fits-all" approach that ignores varying sample complexity. In clinical settings for instance, the waste of computational resources on routine cases is a significant barrier to sustainable AI. In this paper, we introduce the Conformal Adaptive Decision System (CADS), a sequential multi-model algorithm designed to optimize resource allocation by efficiently sampling models based on the estimated data complexity. CADS leverages conformal prediction to quantify image uncertainty at runtime. CADS provides a mathematically grounded framework for balancing the cost-accuracy dilemma that dynamically routes samples through a model cascade, ranging from lightweight "Scout" models to high-capacity "Oracle" architectures. Validated on two datasets, CADS demonstrated superior efficiency and accuracy at a computational cost that can be up to 12 times lower than heavy-model inference. By accurately routing samples based on real-time complexity, CADS ensures high diagnostic reliability while drastically reducing the economic and environmental footprint of AI.