SurgicalMamba achieves SOTA online accuracy on surgical phase recognition benchmarks by adding dual-path SSD, intensity-modulated stepping, and state regramming to Mamba2 while keeping per-frame cost O(d).
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
A multi-stage Delphi consensus with 92 experts catalogs widespread validation pitfalls in surgical AI video analysis across data, metrics, and reporting, supported by a systematic review and empirical experiments.
citing papers explorer
-
SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition
SurgicalMamba achieves SOTA online accuracy on surgical phase recognition benchmarks by adding dual-path SSD, intensity-modulated stepping, and state regramming to Mamba2 while keeping per-frame cost O(d).
-
Current validation practice undermines surgical AI development
A multi-stage Delphi consensus with 92 experts catalogs widespread validation pitfalls in surgical AI video analysis across data, metrics, and reporting, supported by a systematic review and empirical experiments.