Scale-Adaptive Forest Training via an Efficient Feature Sampling Scheme

Peter, Loic; Pauly, Olivier; Chatelain, Pierre; Mateus, Diana; Navab, Nassir

doi:10.1007/978-3-319-24553-9_78

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…Depending on the focus of method design, existing studies in microscopy image classification can be categorized into two groups. The first group focuses on feature extraction, in which customized features are designed (Su et al, 2012;Sparks and Madabhushi, 2013;Peter et al, 2015;Xu et al, 2015;Jiang et al, 2015;Barker et al, 2016) or automated feature learning is conducted (Zhou et al, 2014;Otalora et al, 2015;BenTaieb et al, 2015;Wang et al, 2015). The second group focuses on the classifier design while standard and simple feature descriptors are used.…”

Section: Related Workmentioning

confidence: 99%

Dual discriminative local coding for tissue aging analysis

Song

Zhang

et al. 2017

Medical Image Analysis

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In aging research, morphological age of tissue helps to characterize the effects of aging on different individuals. While currently manual evaluations are used to estimate morphological ages under microscopy, such operation is difficult and subjective due to the complex visual characteristics of tissue images. In this paper, we propose an automated method to quantify morphological ages of tissues from microscopy images. We design a new sparse representation method, namely dual discriminative local coding (DDLC), that classifies the tissue images into different chronological ages. DDLC in- corporates discriminative distance learning and dual-level local coding into the basis model of locality-constrained linear coding thus achieves higher discriminative capability. The morphological age is then computed based on the classification scores. We conducted our study using the publicly avail- able terminal bulb aging database that has been commonly used in existing microscopy imaging research. To represent these images, we also design a highly descriptive descriptor that combines several complementary texture features extracted at two scales. Experimental results show that our method achieves significant improvement in age classification when compared to the existing approaches and other popular classifiers. We also present promising results in quantification of morphological ages.

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Section: Related Workmentioning

confidence: 99%

Dual discriminative local coding for tissue aging analysis

Song

Zhang

et al. 2017

Medical Image Analysis

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“…To obtain π, we use an AdaBoost classifier [10] based on Haar features [11], which we more precisely defined and sampled as in [12]. We denote the stumps h t for t = {1, ..., T }, where T is the number of boosting iterations.…”

Section: Probability Mapmentioning

confidence: 99%

Hands-Free Segmentation of Medical Volumes via Binary Inputs

Dubost

Peter

Rupprecht

et al. 2016

Deep Learning and Data Labeling for Medical Applications

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Abstract. We propose a novel hands-free method to interactively segment 3D medical volumes. In our scenario, a human user progressively segments an organ by answering a series of questions of the form "Is this voxel inside the object to segment? ". At each iteration, the chosen question is defined as the one halving a set of candidate segmentations given the answered questions. For a quick and efficient exploration, these segmentations are sampled according to the Metropolis-Hastings algorithm. Our sampling technique relies on a combination of relaxed shape prior, learnt probability map and consistency with previous answers. We demonstrate the potential of our strategy on a prostate segmentation MRI dataset. Through the study of failure cases with synthetic examples, we demonstrate the adaptation potential of our method. We also show that our method outperforms two intuitive baselines: one based on random questions, the other one being the thresholded probability map.

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“…With these extracted descriptors, supervised classification models such as support vector machine (SVM) [12, 13, 18, 19, 22–24, 27, 29, 31], subspace learning [10, 11, 14–16, 26], multiple instance learning [17, 25] and sparse representation [21, 32] are applied. However, the classification performance is often largely affected by the small number of training data available for bioimage research.…”

Section: Introductionmentioning

confidence: 99%

Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors

et al. 2016

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BackgroundBioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference.ResultsWe evaluate our method on eight different bioimage classification tasks using the publicly available IICBU 2008 database. Each task comprises a separate dataset, and the collection represents typical subcellular, cellular, and tissue level classification problems. Our method demonstrates improved classification accuracy (0.9 to 9%) on six tasks when compared to state-of-the-art approaches. We also find that SDT outperforms the well-known dimension reduction techniques, with for example 0.2 to 13% improvement over linear discriminant analysis.ConclusionsWe present a general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm. Our evaluation on the IICBU 2008 database demonstrates improved performance over the state-of-the-art for six different classification tasks.

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Scale-Adaptive Forest Training via an Efficient Feature Sampling Scheme

Cited by 13 publications

References 15 publications

Dual discriminative local coding for tissue aging analysis

Dual discriminative local coding for tissue aging analysis

Hands-Free Segmentation of Medical Volumes via Binary Inputs

Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors

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