Proceedings ArticleDOI
Histopathological image classification with bilinear convolutional neural networks
Chaofeng Wang,Jun Shi,Qi Zhang,Shihui Ying +3 more
- Vol. 2017, pp 4050-4053
TLDR
A novel BCNN-based method is proposed, which first decomposes histopathological images into hematoxylin and eosin stain components, and then performs BCNN on the decomposed images to fuse and improve the feature representation performance.Abstract:
The computer-aided quantitative analysis for histopathological images has attracted considerable attention. The stain decomposition on histopathological images is usually recommended to address the issue of co-localization or aliasing of tissue substances. Although the convolutional neural networks (CNN) is a popular deep learning algorithm for various tasks on histopathological image analysis, it is only directly performed on histopathological images without considering stain decomposition. The bilinear CNN (BCNN) is a new CNN model for fine-grained classification. BCNN consists of two CNNs, whose convolutional-layer outputs are multiplied with outer product at each spatial location. In this work, we propose a novel BCNN-based method for classification of histopathological images, which first decomposes histopathological images into hematoxylin and eosin stain components, and then perform BCNN on the decomposed images to fuse and improve the feature representation performance. The experimental results on the colorectal cancer histopathological image dataset with eight classes indicate that the proposed BCNN-based algorithm is superior to the traditional CNN.read more
Citations
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Journal ArticleDOI
Machine Learning Methods for Histopathological Image Analysis.
Daisuke Komura,Shumpei Ishikawa +1 more
TL;DR: In this mini-review, the application of digital pathological image analysis using machine learning algorithms is introduced, some problems specific to such analysis are addressed, and possible solutions are proposed.
Journal ArticleDOI
Machine learning methods for histopathological image analysis
Daisuke Komura,Shumpei Ishikawa +1 more
TL;DR: In this paper, the authors introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions, and present a mini-review.
Journal ArticleDOI
BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights
TL;DR: A taxonomy that categorize the breast cancer datasets using either deep learning or traditional models into four different reformulations: Magnification-Specific Binary (MSB),Magnification-Independent Binary (MIB), Magnifying-Specific Multi-category (MSM) and Magnification -Independent Multi- category (MIM) classifications is defined.
Journal ArticleDOI
ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning
TL;DR: This work proposes an accurate, reliable and active (ARA) image classification framework and introduces a new Bayesian Convolutional Neural Network (ARA-CNN) for classifying histopathological images of colorectal cancer, which achieves exceptional classification accuracy, outperforming other models trained on the same dataset.
Journal ArticleDOI
Deep Learning Applied for Histological Diagnosis of Breast Cancer
TL;DR: This study proposes two effective deep transfer learning-based models, which rely on pre-trained DCNN using a large collection of ImageNet dataset images that improve current state-of-the-art systems in both binary and multiclass classification.
References
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Journal Article
Quantification of histochemical staining by color deconvolution
TL;DR: This image analysis algorithm provides a robust and flexible method for objective immunohistochemical analysis of samples stained with up to three different stains using a laboratory microscope, standard RGB camera setup and the public domain program NIH Image.
Journal ArticleDOI
Histopathological Image Analysis: A Review
TL;DR: The recent state of the art CAD technology for digitized histopathology is reviewed and the development and application of novel image analysis technology for a few specific histopathological related problems being pursued in the United States and Europe are described.
Proceedings ArticleDOI
Bilinear CNN Models for Fine-Grained Visual Recognition
TL;DR: Blinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an image descriptor, are proposed.
Posted Content
Bilinear CNN Models for Fine-grained Visual Recognition
TL;DR: This paper proposed bilinear models, which consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an image descriptor, which can model local pairwise feature interactions in a translationally invariant manner.
Proceedings ArticleDOI
Compact Bilinear Pooling
TL;DR: The authors proposed two compact bilinear representations with the same discriminative power as the full Bilinear representation but with only a few thousand dimensions, which allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system.