| Project/Area Number |
20K23343
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
1002:Human informatics, applied informatics and related fields
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| Research Institution | Osaka University |
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Principal Investigator |
LI LIANGZHI 大阪大学, データビリティフロンティア機構, 特任助教(常勤) (10875545)
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| Project Period (FY) |
2020-09-11 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥260,000 (Direct Cost: ¥200,000、Indirect Cost: ¥60,000)
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| Keywords | Few-shot Learning / Semantic Segmentation / Video Understanding / Medical Images / Computer Vision / Surgical Analysis / Deep Learning / Surgical Videos / Unsupervised Learning |
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| Outline of Research at the Start |
This project aims to design an unsupervised learning method to perform spatial and temporal segmentations without humans’ input. We will use the hidden information in surgical videos to better differentiate similar objects and surgical phases, both of which are very difficult for existing solutions.
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| Outline of Final Research Achievements |
As most of the existing automatic surgical video analysis models require a large number of manually labeled data for training, this project aims to design a learning method to perform spatial and temporal segmentations with smaller requirements of humans’ input. During this project, I mainly studied the following sub-topics towards the goal.
1.Surgical images/frames semantic segmentation in a weakly-supervised way. I developed a new training strategy for video semantic segmentation models to utilized unlabeled data to improve their segmentation performance.
2.Surgical videos temporal analysis using no labels. I developed a retrieval-based method to automatically predict surgical duration.
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| Academic Significance and Societal Importance of the Research Achievements |
The establishment of techniques capable of understanding what, where, and when is happening with no requirements on human efforts will ease the task of surgery indexing, clinical training, etc.
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