Exploring the performance and explainability of fine-tuned BERT models for neuroradiology protocol assignment

Salmonn Talebi, Elizabeth Tong, Anna Li, Ghiam Yamin, Greg Zaharchuk, Mohammad R. K. Mofrad

Abstract: Deep learning has demonstrated significant advancements across various domains. However, its implementation in specialized areas, such as medical settings, remains approached with caution. In these high-stake environments, understanding the model's decision-making process is critical. This study assesses the performance of different pretrained Bidirectional Encoder Representations from Transformers (BERT) models and delves into understanding its decision-making within the context of medical image protocol assignment.

External IDs:dblp:journals/midm/TalebiTLYZM24