Structured Model Pruning for Efficient Inference in Computational Pathology
This addresses the challenge of deploying AI models under resource constraints in healthcare, specifically for computational pathology, though it is incremental as it applies existing pruning techniques to a specific domain.
The paper tackles the problem of deploying large AI models in computational pathology by applying model pruning to U-Net-style architectures, achieving at least 70% model compression with negligible performance loss on nuclei instance segmentation and classification tasks.
Recent years have seen significant efforts to adopt Artificial Intelligence (AI) in healthcare for various use cases, from computer-aided diagnosis to ICU triage. However, the size of AI models has been rapidly growing due to scaling laws and the success of foundational models, which poses an increasing challenge to leverage advanced models in practical applications. It is thus imperative to develop efficient models, especially for deploying AI solutions under resource-constrains or with time sensitivity. One potential solution is to perform model compression, a set of techniques that remove less important model components or reduce parameter precision, to reduce model computation demand. In this work, we demonstrate that model pruning, as a model compression technique, can effectively reduce inference cost for computational and digital pathology based analysis with a negligible loss of analysis performance. To this end, we develop a methodology for pruning the widely used U-Net-style architectures in biomedical imaging, with which we evaluate multiple pruning heuristics on nuclei instance segmentation and classification, and empirically demonstrate that pruning can compress models by at least 70% with a negligible drop in performance.