A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images
📖 Description
A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images Tingting Zheng1, Weixing chen2, Shuqin Li1, Hao Quan1, Qun Bai1, Tianhang Nan1, Song Zheng3, Xinghua Gao3, Yue Zhao1 and Xiaoyu Cui1* 1 College of Medicine and Biological Information Engineering, Northeastern University, Shen- yang, 110169, China *Corresponding author :[email address redacted] 2 Shenzhen College of Advanced Technology , University of the Chinese Academy of Sciences, Beijing 100049, China 3 NHC Key Laboratory of Immunodermatology (China Medical University), No.155 Nanjing Bei Street, Heping District, Shenyang, Liao ning Province, 110001, China. Abstract. Deep neural network is a research hotspot on histopathological image analysis, which can improve the efficiency and accuracy of diagnosis for pathologists or be used for disease screening. The whole slide pathological image can reach one gigapixel and contains abundant tissue feature information, whic...
📊 Game Impacts
Not verified in gameWhich variables this event was proposed to move, and in which direction. The magnitudes are held in the corpus but are not shown here, because they have not been verified against the shipped game. They come from pdoom-data. They describe what an event was proposed to do, not what the shipped game does with it. Most events in the corpus are flavour: they are shown for colour and do not move any game variable. Only a small minority reach the systems below, and several of the variables listed here are not read by the game at all yet. Treat this table as a design proposal under review, not as a measurement of play. Corrections and arguments are welcome — the suggestion links at the foot of this page go straight to the data repo.
| Variable | Direction | Condition |
|---|---|---|
| Research | proposed: up | Always |
| Papers | proposed: up | Always |
| Vibey Doom | proposed: up | Always |
💭 Reactions
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🔗 Sources
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