Search Results for author:
Found 2 papers, 0 papers with code
Structured Model Pruning for Efficient Inference in Computational Pathology
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.
Continual Learning for Tumor Classification in Histopathology Images
Recent years have seen great advancements in the development of deep learning models for histopathology image analysis in digital pathology applications, evidenced by the increasingly common deployment of these models in both research and clinical settings.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
