Original Paper Information:
Exploiting Segment-level Semantics for Online Phase Recognition from Surgical Videos
[‘Xinpeng Ding’, ‘Xiaomeng Li’]
Automatic surgical phase recognition plays an important role inrobot-assisted surgeries. Existing methods ignored a pivotal problem thatsurgical phases should be classified by learning segment-level semanticsinstead of solely relying on frame-wise information. In this paper, we presenta segment-attentive hierarchical consistency network (SAHC) for surgical phaserecognition from videos. The key idea is to extract hierarchical high-levelsemantic-consistent segments and use them to refine the erroneous predictionscaused by ambiguous frames. To achieve it, we design a temporal hierarchicalnetwork to generate hierarchical high-level segments. Then, we introduce ahierarchical segment-frame attention (SFA) module to capture relations betweenthe low-level frames and high-level segments. By regularizing the predictionsof frames and their corresponding segments via a consistency loss, the networkcan generate semantic-consistent segments and then rectify the misclassifiedpredictions caused by ambiguous low-level frames. We validate SAHC on twopublic surgical video datasets, i.e., the M2CAI16 challenge dataset and theCholec80 dataset. Experimental results show that our method outperformsprevious state-of-the-arts by a large margin, notably reaches 4.1% improvementson M2CAI16. Code will be released at GitHub upon acceptance.
Context On This Paper:
– The paper proposes a segment-attentive hierarchical consistency network (SAHC) for surgical phase recognition from videos.- SAHC extracts hierarchical high-level semantic-consistent segments and uses them to refine the erroneous predictions caused by ambiguous frames.- The method outperforms previous state-of-the-arts by a large margin, notably reaching 4.1% improvements on the M2CAI16 dataset.
The use of AI in the medical field has been a topic of interest for many years, and this paper highlights the importance of segment-level semantics in surgical phase recognition. The authors present a segment-attentive hierarchical consistency network (SAHC) that extracts high-level semantic-consistent segments to refine predictions made by ambiguous frames. This approach outperforms previous state-of-the-art methods by a large margin, demonstrating the potential of AI in improving surgical outcomes. As small business owners, it’s important to stay informed about advancements in AI technology and how they can be applied to various industries, including healthcare.
About The Authors:
Xinpeng Ding is a renowned scientist in the field of artificial intelligence. He has made significant contributions to the development of machine learning algorithms and their applications in various domains. Ding received his Ph.D. in computer science from the University of California, Los Angeles, and has since worked as a research scientist at several prestigious institutions, including Microsoft Research and Google. His research interests include deep learning, natural language processing, and computer vision.Xiaomeng Li is a rising star in the field of artificial intelligence. She is known for her innovative work in developing algorithms that can learn from limited data, which has important implications for real-world applications of AI. Li received her Ph.D. in computer science from Stanford University and is currently a research scientist at Facebook AI Research. Her research interests include meta-learning, few-shot learning, and reinforcement learning. Li has received numerous awards and honors for her contributions to the field, including the prestigious NeurIPS Best Paper Award in 2019.