Friends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition
This improves surgical video analysis for operating rooms and assessment platforms, though it appears incremental as it builds on existing spatial-temporal methods.
The authors tackled surgical phase recognition by proposing MS-AST and MS-ASCT transformers, achieving 95.26% and 96.15% accuracy on the Cholec80 dataset for online and offline tasks, respectively, setting new state-of-the-art results.
Automatic surgical phase recognition is a core technology for modern operating rooms and online surgical video assessment platforms. Current state-of-the-art methods use both spatial and temporal information to tackle the surgical phase recognition task. Building on this idea, we propose the Multi-Scale Action Segmentation Transformer (MS-AST) for offline surgical phase recognition and the Multi-Scale Action Segmentation Causal Transformer (MS-ASCT) for online surgical phase recognition. We use ResNet50 or EfficientNetV2-M for spatial feature extraction. Our MS-AST and MS-ASCT can model temporal information at different scales with multi-scale temporal self-attention and multi-scale temporal cross-attention, which enhances the capture of temporal relationships between frames and segments. We demonstrate that our method can achieve 95.26% and 96.15% accuracy on the Cholec80 dataset for online and offline surgical phase recognition, respectively, which achieves new state-of-the-art results. Our method can also achieve state-of-the-art results on non-medical datasets in the video action segmentation domain.