Hybrid deep autoencoder with Curvature Gaussian for detection of various types of cells in bone marrow trephine biopsy images

@article{Song2017HybridDA,
  title={Hybrid deep autoencoder with Curvature Gaussian for detection of various types of cells in bone marrow trephine biopsy images},
  author={Tzu-Hsi Song and Victor Sanchez and Hesham EIDaly and Nasir M. Rajpoot},
  journal={2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)},
  year={2017},
  pages={1040-1043}
}
Automated cell detection is a critical step for a number of computer-assisted pathology related image analysis algorithm. However, automated cell detection is complicated due to the variable cytomorphological and histological factors associated with each cell. In order to efficiently resolve the challenge of automated cell detection, deep learning strategies are widely applied and have recently been shown to be successful in histopathological images. In this paper, we concentrate on bone marrow… 

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References

SHOWING 1-10 OF 13 REFERENCES
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
TLDR
A Stacked Sparse Autoencoder, an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer and out-performed nine other state of the art nuclear detection strategies.
Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images
TLDR
A Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection and a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei are proposed.
Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential
TLDR
This study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols.
Local isotropic phase symmetry measure for detection of beta cells and lymphocytes
TLDR
Experimental results show that the algorithm performs better than human experts for detection of two types of specific cells in histology images, cells in mouse pancreatic sections and lymphocytes in human breast tissue, and outperforms the best reported results for the latter.
A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution
TLDR
The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.
A Non-Linear Mapping Approach to Stain Normalisation in Digital Histopathology Images using Image-Specific Colour Deconvolution
TLDR
The experimental results suggest that the paradigm of colour normalisation, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.
Scale and Curvature Invariant Ridge Detector for Tortuous and Fragmented Structures
TLDR
A novel ridge detector is proposed, SCIRD, which is simultaneously rotation, scale and curvature invariant, and relaxes shape assumptions to achieve enhancement of target image structures inSegmenting dendritic trees and corneal nerve fibres.
Bone marrow pathology in essential thrombocythemia: interobserver reliability and utility for identifying disease subtypes.
TLDR
The results show that histologic criteria described in the WHO classification are difficult to apply reproducibly and question the validity of distinguishing true ET from prefibrotic myelofibrosis on the basis of subjective morphologic criteria.
Coupled Deep Autoencoder for Single Image Super-Resolution
TLDR
A data-driven model coupled deep autoencoder (CDA) that simultaneously learns the intrinsic representations of LR and HR image patches and a big-data-driven function that precisely maps these LR representations to their corresponding HR representations is developed.
Autoencoders, Unsupervised Learning, and Deep Architectures
  • P. Baldi
  • Computer Science
    ICML Unsupervised and Transfer Learning
  • 2012
TLDR
The framework sheds light on the different kinds of autoencoders, their learning complexity, their horizontal and vertical composability in deep architectures, their critical points, and their fundamental connections to clustering, Hebbian learning, and information theory.
...
...