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

• URL: http://arxiv.org/abs/2401.11644v1
• Date: Mon, 22 Jan 2024 01:34:03 GMT
• Title: Friends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition
• Authors: Bokai Zhang, Jiayuan Meng, Bin Cheng, Dean Biskup, Svetlana Petculescu, Angela Chapman
• Abstract summary: We propose the Multi-Scale Action Transformer (MS-AST) for offline surgical phase recognition and the Multi-Scale Action Causal Transformer (MS-ASCT) for online surgical phase recognition. Our method can achieve 95.26% and 96.15% accuracy on the Cholec80 dataset for online and offline surgical phase recognition, respectively.
• Score: 2.10407185597278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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