{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/must-multi-scale-transformers-for-surgical","title":"MuST: Multi-Scale Transformers for Surgical Phase Recognition","arxiv_id":"2407.17361","date":"2024-07-24","proceeding":null,"authors":["Alejandra Pérez","Santiago Rodríguez","Nicolás Ayobi","Nicolás Aparicio","Eugénie Dessevres","Pablo Arbeláez"],"abstract":"Phase recognition in surgical videos is crucial for enhancing computer-aided surgical systems as it enables automated understanding of sequential procedural stages. Existing methods often rely on fixed temporal windows for video analysis to identify dynamic surgical phases. Thus, they struggle to simultaneously capture short-, mid-, and long-term information necessary to fully understand complex surgical procedures. To address these issues, we propose Multi-Scale Transformers for Surgical Phase Recognition (MuST), a novel Transformer-based approach that combines a Multi-Term Frame encoder with a Temporal Consistency Module to capture information across multiple temporal scales of a surgical video. Our Multi-Term Frame Encoder computes interdependencies across a hierarchy of temporal scales by sampling sequences at increasing strides around the frame of interest. Furthermore, we employ a long-term Transformer encoder over the frame embeddings to further enhance long-term reasoning. MuST achieves higher performance than previous state-of-the-art methods on three different public benchmarks.","url_abs":"https://arxiv.org/abs/2407.17361v1","url_pdf":"https://arxiv.org/pdf/2407.17361v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"must-multi-scale-transformers-for-surgical","repo_url":"https://github.com/BCV-Uniandes/MuST","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"online-surgical-phase-recognition","task_name":"Online surgical phase recognition"},{"task_slug":"surgical-phase-recognition","task_name":"Surgical phase recognition"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/surgical-phase-recognition-on-cholec80-1","task":"Surgical phase recognition","dataset":"Cholec80","model":"MuST","rank_in_archive_order":3,"of":6,"metrics":{"F1":"85.57"},"uses_additional_data":false},{"leaderboard":"/sota/surgical-phase-recognition-on-grasp","task":"Surgical phase recognition","dataset":"GraSP","model":"MuST","rank_in_archive_order":1,"of":2,"metrics":{"mAP":"79.14"},"uses_additional_data":false},{"leaderboard":"/sota/surgical-phase-recognition-on-heichole","task":"Surgical phase recognition","dataset":"HeiChole Benchmark","model":"MuST","rank_in_archive_order":1,"of":5,"metrics":{"F1":"77.25"},"uses_additional_data":false},{"leaderboard":"/sota/surgical-phase-recognition-on-misaw","task":"Surgical phase recognition","dataset":"MISAW","model":"MuST","rank_in_archive_order":1,"of":3,"metrics":{"mAP":"98.08"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2407.17361","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}