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Automatic surgical phase recognition is a core technology for modernoperating rooms and online surgical video assessment platforms. Currentstate-of-the-art methods use both spatial and temporal information to tacklethe surgical phase recognition task. Building on this idea, we propose theMulti-Scale Action Segmentation Transformer (MS-AST) for offline surgical phaserecognition and the Multi-Scale Action Segmentation Causal Transformer(MS-ASCT) for online surgical phase recognition. We use ResNet50 orEfficientNetV2-M for spatial feature extraction. Our MS-AST and MS-ASCT canmodel temporal information at different scales with multi-scale temporalself-attention and multi-scale temporal cross-attention, which enhances thecapture of temporal relationships between frames and segments. We demonstratethat our method can achieve 95.26% and 96.15% accuracy on the Cholec80 datasetfor online and offline surgical phase recognition, respectively, which achievesnew state-of-the-art results. Our method can also achieve state-of-the-artresults on non-medical datasets in the video action segmentation domain.

Friends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition