Shuangchun Gui
4 Papers
2 Citations
Shuangchun Gui is an academic researcher. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 2, co-authored 3 publications.
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Papers
CholecTriplet2022: Show me a tool and tell me the triplet - an endoscopic vision challenge for surgical action triplet detection
Saurav Sharma,Deepak Alapatt,Kun Yuan,Wolfgang Reiter,Amine Yamlahi,Guoyan Zheng,Helena R. Torres,Satoshi Kondo,Felix Holm,Shuangchun Gui,Sista Raviteja,Rachana Sathish,B N Bhattarai,Guo Rui,Melanie Schellenberg,Zhenkun Wang,Shrawan Kumar Thapa,Thuy Nuong Tran,Jaime C. Fonseca,Pietro Mascagni,Chinedu Innocent Nwoye,Tong Yu,Aditya Murali,Armine Vardazaryan,Jonas Hajek,Finn-Henri Smidt,Xiaoyang Zou,Bruno Oliveira,Satoshi Kasai,Ege Özsoy,Han Li,Pranav Poudel,Ziheng Wang,Joao L. Vilacca,Tobias Czempiel,Debdoot Sheet,Max Berniker,Patrick Godau,Pedro Morais,S. Regmi,Jan-Hinrich Nolke,Estevão Lima,Eduard Vazquez,Lena Maier-Hein,Nassir Navab,Barbara Seeliger,Cristians Gonzalez,Didier Mutter,Nicolas Padoy +48 more
TL;DR: The CholecTriplet2022 challenge as discussed by the authors extended surgical action triplet modeling from recognition to detection, which includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and modeling of each tool-activity in the form of triplet.
MT4MTL-KD: A Multi-teacher Knowledge Distillation Framework for Triplet Recognition.
Shuangchun Gui,Zhenkun Wang,Jixiang Chen,Xun Zhou,Chen Zhang,Yi Cao +5 more
TL;DR: A novel multi-teacher knowledge distillation framework formulti-task triplet learning, known as MT4MTL-KD, which leverages teacher models trained on less imbalanced sub-tasks to assist multi-task student learning for triplet recognition and proposes a novel feature attention module (FAM).
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CholecTriplet2022: Show me a tool and tell me the triplet - an endoscopic vision challenge for surgical action triplet detection
Chinedu Innocent Nwoye,Tong Yu,Saurav Sharma,Aditya Murali,Deepak Alapatt,Kun Yuan,Jonas Hajek,Wolfgang Reiter,Xiaoyang Zou,Bruno Oliveira,Helena R. Torres,Felix Holm,Ege Özsoy,Sista Raviteja,Rachana Sathish,Pranav Poudel,Guo Rui,Tobias Czempiel,Zhenkun Wang,Shrawan Kumar Thapa,Thuy Nuong Tran,Lena Maier-Hein,Nassir Navab,Cristians Gonzalez,Armine Vardazaryan,Amine Yamlahi,Finn-Henri Smidt,Guoyan Zheng,Satoshi Kondo,Satoshi Kasai,Shuangchun Gui,Han Li,B N Bhattarai,Ziheng Wang,Melanie Schellenberg,Joao L. Vilacca,Debdoot Sheet,Max Berniker,Patrick Godau,Pedro Morais,S. Regmi,Jaime C. Fonseca,Jan-Hinrich Nolke,Estevão Lima,Eduard Vazquez,Pietro Mascagni,Barbara Seeliger,Didier Mutter,Nicolas Padoy +48 more
TL;DR: The CholecTriplet2022 challenge as mentioned in this paper extended surgical action triplet modeling from recognition to detection, which includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and modeling of each tool-activity in the form of triplet.
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Spectrum and Style Transformation Framework for Omni-Domain COVID-19 Diagnosis
TL;DR: Wang et al. as discussed by the authors proposed a spectrum and style transformation framework for omni-domain COVID-19 diagnosis, which helps to discover the discriminating features of each domain to recognize the in-domain data.
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