11,973 research outputs found
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.Comment: Accepted by Journal of Structural Biolog
RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
Efficient and precise classification of histological cell nuclei is of utmost
importance due to its potential applications in the field of medical image
analysis. It would facilitate the medical practitioners to better understand
and explore various factors for cancer treatment. The classification of
histological cell nuclei is a challenging task due to the cellular
heterogeneity. This paper proposes an efficient Convolutional Neural Network
(CNN) based architecture for classification of histological routine colon
cancer nuclei named as RCCNet. The main objective of this network is to keep
the CNN model as simple as possible. The proposed RCCNet model consists of only
1,512,868 learnable parameters which are significantly less compared to the
popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The
experiments are conducted over publicly available routine colon cancer
histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet
model are compared with five state-of-the-art CNN models in terms of the
accuracy, weighted average F1 score and training time. The proposed method has
achieved a classification accuracy of 80.61% and 0.7887 weighted average F1
score. The proposed RCCNet is more efficient and generalized terms of the
training time and data over-fitting, respectively.Comment: Published in ICARCV 201
Studying Parallel Evolutionary Algorithms: The cellular Programming Case
Parallel evolutionary algorithms, studied to some extent over the past few years, have proven empirically worthwhile—though there seems to be lacking a better understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, presenting a number of statistical measures, both at the genotypic and phenotypic levels. We demonstrate the application and utility of these measures on a specific example, that of the cellular programming evolutionary algorithm, when used to evolve solutions to a hard problem in the cellular-automata domain, known as synchronization
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