Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. ,  examine the use of a crowdsourcing platform for the distribution of super-pixel ak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-ighted MR images. Using this approach  report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research.
['Martin Rajchl', 'Matthew C. H. Lee', 'Franklin Schrans', 'Alice Davidson', 'Jonathan Passerat-Palmbach', 'Giacomo Tarroni', 'Amir Alansary', 'Ozan Oktay', 'Bernhard Kainz', 'Daniel Rueckert']
I am writing a research paper on a idea Learning under Distributed Weak Supervision
[{"from": "human", "value": "I am writing a research paper on a idea Learning under Distributed Weak Supervision"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. ,  examine the use of a crowdsourcing platform for the distribution of super-pixel ak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-ighted MR images. Using this approach  report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research."}]