Abstract
Surgical phase recognition is an important topic of Computer Assisted Surgery (CAS) systems. In the complicated surgical procedures, there are lots of hard frames that have indistinguishable visual features but are assigned with different labels. Prior works try to classify hard frames along with other simple frames indiscriminately, which causes various problems. Different from previous approaches, we take hard frames as mislabeled samples and find them in the training set via data cleansing strategy. Then, we propose an Online Hard Frame Mapper (OHFM) to handle the detected hard frames separately. We evaluate our solution on the M2CAI16 Workflow Challenge dataset and the Cholec80 dataset and achieve superior results. (The code is available at https://github.com/ChinaYi/miccai19).
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Acknowledgement
This work was partially supported by the National Basic Research Program of China (973 Program) under contract 2015CB351803, the Natural Science Foundation of China under contracts 61572042 and 61527804. We also acknowledge the Clinical Medicine Plus X-Young Scholars Project, and High-Performance Computing Platform of Peking University for providing computational resources.
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Yi, F., Jiang, T. (2019). Hard Frame Detection and Online Mapping for Surgical Phase Recognition. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11768. Springer, Cham. https://doi.org/10.1007/978-3-030-32254-0_50
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DOI: https://doi.org/10.1007/978-3-030-32254-0_50
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