Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition
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Summary
The proposed system was developed to solve a particular problem on maize cells, namely to determine the composition of cell wall ingrowths in endosperm transfer cells, and determined that the LoG detector alone attained more than 84% of accuracy with the F-measure.
- Type
- preprint
- Published
- 2017-12-07
- Cited by
- 0
- References
- 40
- Access
- Open access
- OpenAlex
- https://openalex.org/W2774000834
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:20528119
Keywords
Immunogold labelling, Computer science, Artificial intelligence, Biological system, Pattern recognition (psychology)
References
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- Theano: new features and speed improvements
- Improving transfer learning accuracy by reusing Stacked Denoising Autoencoders
- Improving Performance on Problems with Few Labelled Data by Reusing Stacked Auto-Encoders
- The relationship between Precision-Recall and ROC curves
- Miniature1-Encoded Cell Wall Invertase Is Essential for Assembly and Function of Wall-in-Growth in the Maize Endosperm Transfer Cell1[W][OA]
- Circular hough transform for roundness measurement of objects
- Using Different Cost Functions to Train Stacked Auto-Encoders
- An empirical evaluation of deep architectures on problems with many factors of variation
- Extraction of spots in biological images using multiscale products
- The Laplacian of Gaussian and arbitrary z-crossings approach applied to automated single particle reconstruction.
- Detecting particles in cryo-EM micrographs using learned features.
- Extracting and composing robust features with denoising autoencoders
- DoG Picker and TiltPicker: software tools to facilitate particle selection in single particle electron microscopy
- Development of flange and reticulate wall ingrowths in maize (Zea mays L.) endosperm transfer cells
- Icy: an open bioimage informatics platform for extended reproducible research
- Scale-Space Theory : A Basic Tool for Analysing Structures at Different Scales
- Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective
- Is EM dead?
- Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features
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