Bag-of-visual-ngrams for histopathology image classification

López-Monroy, Adrián Pastor; Montes-y-Gómez, Manuel; Cruz-Roa, Ángel; González, Fabio A.

doi:10.1117/12.2034113

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…, k (1)-grams. Finally, it is worth knowing that we perform a set of specific experiments that highlights the general usefulness of visual n-grams, but a detailed study of the parameterization and other applications can be found in [10]. First Experiment: Table 2, thought the first row, shows the results of evaluating the traditional BoVW (Unigramas).…”

Section: Preliminary Results On Image Clasification Using Sequences Omentioning

confidence: 99%

Image Classification through Text Mining techniques: a Proposal

López-Monroy¹,

Montes-y-Gómez²,

González³

2014

RCS

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Image classification is an important task for the organization and analysis of visual information. According to the literature one of the most important concepts is the visual word ; a visual element that represents a set of visual-similar regions. The Bag-of-Visual Words (BoVW) is one of the most widely used approaches in High Level Computer Vision (HLCV). The BoVW is an histogram of the occurrence of visual words in each image, which is in some way inspired by the Bag-of-Words (BoW) used in Natural Language Processing (NLP). In spite of the success of BoVW, it has the same limitations of BoW (e.g., the overlook of the spatial context). In this research proposal we bear in mind the successful evidence of visual words in HLCV, and we take the analogy of visual-textual words to a new higher level. This is, by designing methods inspired in NLP, we aim to consider contextual (e.g., spatial, sequential), and high level (e.g., semantic) information among visual words. However, bringing NLP like approaches pose several nontrivial problems, for example: i) the definition of analogous attributes (visual-textual), ii) a suitable strategy to interpret images; documents can be read only in one direction, but in images we have a 2D plane without an specific way to read them, iii) the way to extract high level information (e.g., semantic). This paper presents the proposed research methodology and through preliminary results, we provide strong evidence of the feasibility of this research. For this, a popular NLP technique is used to improve the BoVW; the Bag-of-Visual n-grams (BoVN). The idea is evaluated in the challenging task of Histopathology image classification overcoming the BoVW and an state-of-the-art approach based in language models.

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Section: Preliminary Results On Image Clasification Using Sequences Omentioning

confidence: 99%

Image Classification through Text Mining techniques: a Proposal

López-Monroy¹,

Montes-y-Gómez²,

González³

2014

RCS

Self Cite

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“…The use of visual codebooks as basis for summarizing images has seen a lot of applications since it has been initially proposed [30], being successfully used in analysis of digital pathology images as well [31,32]. Generally, the method requires constructing a dictionary of visual words (a codebook) that will be used as templates for summarizing the images.…”

Section: Bag Of Visual Wordsmentioning

confidence: 99%

Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis

2020

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A novel deep autoencoder architecture is proposed for the analysis of histopathology images. Its purpose is to produce a disentangled latent representation in which the structure and colour information are confined to different subspaces so that stain-independent models may be learned. For this, we introduce two constraints on the representation which are implemented as a classifier and an adversarial discriminator. We show how they can be used for learning a latent representation across haematoxylin-eosin and a number of immune stains. Finally, we demonstrate the utility of the proposed representation in the context of matching image patches for registration applications and for learning a bag of visual words for whole slide image summarization.

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“…In addition, we propose a genetic program to learn weighting schemes by combining a set of primitives. One should note that there are efforts for improving the bag of visual words in several directions, most notably, great advances have been obtained for incorporating spatial information [6], [4], [5], [17], [18]. The term-weighting schemes developed in this work can also be applied with the previous extensions.…”

Section: The Bag Of Visual Words Representationmentioning

confidence: 99%

Improving bag of visual words representations with genetic programming

Martinez-Carraza

Escalera

Ponce-López

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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The bag of visual words is a well established repre sentation in diverse computer vision problems. Ta king inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains.In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm.

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Bag-of-visual-ngrams for histopathology image classification

Cited by 9 publications

References 21 publications

Image Classification through Text Mining techniques: a Proposal

Image Classification through Text Mining techniques: a Proposal

Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis

Improving bag of visual words representations with genetic programming

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