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The problem of content-based image and video retrieval with textual queries is often posed as that of visual concept classification, where classifiers for a set of predetermined visual concepts are trained using a set of manually annotated images.
Thus to study filopodia under several different conditions can require tens, if not hundreds, of manually annotated images.
The proposed computational pipeline was (i) evaluated with synthetic and manually annotated images, (ii) compared with alternative methods and (iii) applied to a dataset of MCF10A, MCF7, MDA-MB-231 and MDA-MB-468 cell lines that are fixed in culture at days 2, 5 and 7 for quantifying morphogenesis.
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The second plot shows the result when the DCNN was trained on well 1 and tested on well 2. The magenta plot shows the comparison done on the single manually annotated image site while the blue box plot shows the result when compared with all the nine image sites in well 2. Similarly, the third plot corresponds to the result when the DCNN was trained on well 1 and tested on well 3.
We focus on the way how people manually annotate images (human aspects of image perception).
By integrating unlabeled data in the learning phase, a new sample can help fine-tune parameters of a model built from a previous sample, which can improve accuracy without requiring users to manually annotate images in the new sample.
S.C. and A.L. first proposed the idea of applying image recognition to SEM image, S.C. and R.A. planned the overall work, M.H.M. and R.A. manually annotated the images and performed the retraining on the datasets.
The parameters of the model are estimated from a set of manually annotated (training) images.
We manually annotated these images for filopodia, as described previously [ 5].
Filopodia can be detected by eye using confocal fluorescence microscopy, and they can be manually annotated in images to quantify filopodia parameters.
We manually annotated one image, unused in training set creation, out of the nine images.
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