首页|Investigators from Sichuan University Zero in on Helicobacter pylori (Handling Noisy Labels Via One-step Abductive Multi-target Learning and Its Application To Helicobacter Pylori Segmentation)
Investigators from Sichuan University Zero in on Helicobacter pylori (Handling Noisy Labels Via One-step Abductive Multi-target Learning and Its Application To Helicobacter Pylori Segmentation)
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NETL
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Springer Nature
Data detailed on Gram-Negative Bacteria - Helicobacter pylori have been presented. According to news reporting from Chengdu, People's Republic of China, by NewsRx journalists, research stated, "Learning from noisy labels is an important concern in plenty of real-world scenarios. Various approaches for this concern first make corrections corresponding to potentially noisy-labeled instances, and then update predictive model with information of the made corrections." Financial supporters for this research include Sichuan Science and Technology Program, The 1<middle dot>3<middle dot>5 project for disciplines of excellence Clinical Research Incubation Project, West China Hospital, Sichuan University, China, The 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University, China, Technological Innovation Project of Chengdu New Industrial Technology Research Institute.
ChengduPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesEpsilonproteobacteriaGram-Negative BacteriaHealth and MedicineHelicobacterHelicobacter pyloriMachine LearningProteobacteriaSichuan University
NETL
NSTL
Springer Nature
