Book Chapter10.1007/978-3-030-63830-6_26
Encoder-Decoder Based CNN Structure for Microscopic Image Identification
Dawid Połap,Marcin Wozniak,Marcin Korytkowski,Rafal Scherer +3 more
- 18 Nov 2020
- pp 301-312
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TL;DR: This research proposes a novel solution based on convolutional auto-encoders and additional two-dimensional image processing techniques to achieve better efficiency in the detection and classification of small objects in images obtained from various microscopes.
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Abstract: The significant development of classifiers has made object detection and classification by using neural networks more effective and more straightforward. Unfortunately, there are images where these operations are still difficult due to the overlap of objects or very blurred contours. An example is images obtained from various microscopes, where bacteria or other biological structures can merge, or even have different shapes. To this end, we propose a novel solution based on convolutional auto-encoders and additional two-dimensional image processing techniques to achieve better efficiency in the detection and classification of small objects in such images. In our research, we have included elements such as very weak contours of shapes that may result from the merging of biological objects. The presented method was compared with others, such as a faster recurrent convolutional neural network to indicate the advantages of the proposed solution.
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References
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Liang-Chieh Chen,Yukun Zhu,George Papandreou,Florian Schroff,Hartwig Adam +4 more
- 08 Sep 2018
TL;DR: This work extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries and applies the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network.
•Posted Content
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Luis Perez,Jason Wang +1 more
TL;DR: A method to allow a neural net to learn augmentations that best improve the classifier, which is called neural augmentation is proposed, and the successes and shortcomings of this method are discussed.
•Posted Content
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Luis Perez,Jason Wang +1 more
TL;DR: In this article, the authors explore and compare multiple solutions to the problem of data augmentation in image classification and propose a method to allow a neural net to learn augmentations that best improve the classifier, which they call neural augmentation.
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Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
TL;DR: Wang et al. as mentioned in this paper proposed a cascaded RNN model using gated recurrent units (GRUs) to explore the redundant and complementary information of hyperspectral images (HSIs).
Convolutional neural networks for hyperspectral image classification
Shiqi Yu,Sen Jia,Chunyan Xu +2 more
TL;DR: An efficient CNN architecture has been proposed to boost its discriminative capability for hyperspectral image classification, in which the original data is used as the input and the final CNN outputs are the predicted class-related results.
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