Surgical Instruction Generation with Transformers

Authors: Zhang, J., Nie, Y., Chang, J. and Zhang, J.J.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 12904 LNCS

Pages: 290-299

eISSN: 1611-3349

ISBN: 9783030872014

ISSN: 0302-9743

DOI: 10.1007/978-3-030-87202-1_28

Abstract:

Automatic surgical instruction generation is a prerequisite towards intra-operative context-aware surgical assistance. However, generating instructions from surgical scenes is challenging, as it requires jointly understanding the surgical activity of current view and modelling relationships between visual information and textual description. Inspired by the neural machine translation and imaging captioning tasks in open domain, we introduce a transformer-backboned encoder-decoder network with self-critical reinforcement learning to generate instructions from surgical images. We evaluate the effectiveness of our method on DAISI dataset, which includes 290 procedures from various medical disciplines. Our approach outperforms the existing baseline over all caption evaluation metrics. The results demonstrate the benefits of the encoder-decoder structure backboned by transformer in handling multimodal context.

http://eprints.bournemouth.ac.uk/36118/

Source: Scopus

Surgical Instruction Generation with Transformers

Authors: Zhang, J., Nie, Y., Chang, J. and Zhang, J.J.

Journal: MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT IV

Volume: 12904

Pages: 290-299

eISSN: 1611-3349

ISBN: 978-3-030-87201-4

ISSN: 0302-9743

DOI: 10.1007/978-3-030-87202-1_28

http://eprints.bournemouth.ac.uk/36118/

Source: Web of Science (Lite)