Structured Model Pruning for Efficient Inference in Computational
  Pathology
Mohammed Adnan Qinle Ba Nazim Shaikh Shivam Kalra Satarupa Mukherjee Auranuch Lorsakul
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.