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000303404 1001_ $$aDas, Adrito$$b0
000303404 245__ $$aPitVis-2023 challenge: Workflow recognition in videos of endoscopic pituitary surgery.
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000303404 520__ $$aThe field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery, including: which surgical steps are performed; and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery or during live surgery. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a particularly challenging task when compared to other minimally invasive surgeries due to: the smaller working space, which limits and distorts vision; and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. The top performing model for step recognition utilised a transformer based architecture, uniquely using an autoregressive decoder with a positional encoding input. The top performing model for instrument recognition utilised a spatial encoder followed by a temporal encoder, which uniquely used a 2-layer temporal architecture. In both cases, these models outperformed purely spatial based models, illustrating the importance of sequential and temporal information. This PitVis-2023 therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset. Benchmark results are provided in the paper, and the dataset is publicly available at: https://doi.org/10.5522/04/26531686.
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000303404 650_7 $$2Other$$aEndoscopic vision
000303404 650_7 $$2Other$$aInstrument recognition
000303404 650_7 $$2Other$$aStep recognition
000303404 650_7 $$2Other$$aSurgical AI
000303404 650_7 $$2Other$$aSurgical vision
000303404 650_7 $$2Other$$aWorkflow analysis
000303404 7001_ $$aKhan, Danyal Z$$b1
000303404 7001_ $$aPsychogyios, Dimitrios$$b2
000303404 7001_ $$aZhang, Yitong$$b3
000303404 7001_ $$aHanrahan, John G$$b4
000303404 7001_ $$aVasconcelos, Francisco$$b5
000303404 7001_ $$aPang, You$$b6
000303404 7001_ $$aChen, Zhen$$b7
000303404 7001_ $$aWu, Jinlin$$b8
000303404 7001_ $$aZou, Xiaoyang$$b9
000303404 7001_ $$aZheng, Guoyan$$b10
000303404 7001_ $$aQayyum, Abdul$$b11
000303404 7001_ $$aMazher, Moona$$b12
000303404 7001_ $$aRazzak, Imran$$b13
000303404 7001_ $$aLi, Tianbin$$b14
000303404 7001_ $$aYe, Jin$$b15
000303404 7001_ $$aHe, Junjun$$b16
000303404 7001_ $$aPłotka, Szymon$$b17
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000303404 7001_ $$aKondo, Satoshi$$b22
000303404 7001_ $$aKasai, Satoshi$$b23
000303404 7001_ $$aHirasawa, Kousuke$$b24
000303404 7001_ $$aRivoir, Dominik$$b25
000303404 7001_ $$aSpeidel, Stefanie$$b26
000303404 7001_ $$aPérez, Alejandra$$b27
000303404 7001_ $$aRodriguez, Santiago$$b28
000303404 7001_ $$aArbeláez, Pablo$$b29
000303404 7001_ $$aStoyanov, Danail$$b30
000303404 7001_ $$aMarcus, Hani J$$b31
000303404 7001_ $$aBano, Sophia$$b32
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