Structured Model Pruning for Efficient Inference in Computational Pathology

arXiv:2404.08831v1 Announce Type: cross
Abstract: 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 …

abstract ai models artificial artificial intelligence arxiv cases computational computer cs.cv cs.lg diagnosis eess.iv foundational foundational models healthcare however inference intelligence laws pathology pruning scaling success type use cases