Enhancing generalization in zero-shot multi-label endoscopic instrument classification.

ABSTRACT:

Purpose

Recognizing previously unseen classes with neural networks is a significant challenge due to their limited generalization capabilities. This issue is particularly critical in safety-critical domains such as medical applications, where accurate classification is essential for reliability and patient safety. Zero-shot learning methods address this challenge by utilizing additional semantic data, with their performance relying heavily on the quality of the generated embeddings.

Methods

This work investigates the use of full descriptive sentences, generated by a Sentence-BERT model, as class representations, compared to simpler category-based word embeddings derived from a BERT model. Additionally, the impact of z-score normalization as a post-processing step on these em

SUBMITTER: Maerkl R 

PROVIDER: S-EPMC12350423 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature