Quasi-supervised learning for biomedical data analysis

Karaçali, B.

doi:10.1016/j.patcog.2010.04.024

Cited by 17 publications

(28 citation statements)

References 25 publications

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“…Quasi-supervised learning (QSL), is a machine-learning technique that uses both indirectly labelled and unlabelled data as training datasets and does not require manually labelled groundtruth vector datasets for recognition (Karacali, 2010). In a binary recognition setting, the algorithm is provided with two datasets: a reference dataset C 0 containing samples of one class only, and a mixed dataset C 1 containing an unlabelled collection of samples.…”

Section: Quasi-supervised Learningmentioning

confidence: 99%

“…The reasoning and the verification of the cost functional were explained in greater detail in Karacali (2010).…”

Section: Quasi-supervised Learningmentioning

confidence: 99%

“…Quasi-supervised learning (QSL) is a statistical learning algorithm that contrasts two datasets by computing estimates for the posterior probabilities of their samples of belonging in either dataset (Karacali, 2010). The QSL method addresses an identification problem where labelled samples are available from one class only, in a reference dataset.…”

Section: Introductionmentioning

confidence: 99%

“…In performance evaluation experiments on synthetic datasets, QSL method outperformed alternative strategies based on SVM classification and minimum spanning trees for varying dataset size, overlap, and dimensionality (Karacali, 2010). The QSL algorithm was successfully operated on a limited number of colon histopathology slide image database (Onder et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning

Onder¹,

Sarıoğlu²,

Karaçali³

2013

Micron

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a b s t r a c tQuasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological images before. The purpose of this study is to evaluate the performance of the method to identify colorectal tissues with or without adenocarcinoma. Light microscopic digital images from histopathological sections were obtained from 30 colorectal radical surgery materials including adenocarcinoma and non-neoplastic regions. The texture features were extracted by using local histograms and co-occurrence matrices. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly, and the other containing an unlabeled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues for conventional supervised learning and significantly reduces the expert intervention. Several texture feature vector datasets corresponding to different extraction parameters were tested within the proposed framework. The Independent Component Analysis dimensionality reduction approach was also identified as the one improving the labelling performance evaluated in this series. In this series, the proposed method was applied to the dataset of 22,080 vectors with reduced dimensionality 119 from 132. Regions containing cancer tissue could be identified accurately having false and true positive rates up to 19% and 88% respectively without using manually labelled ground-truth datasets in a quasi-supervised strategy. The resulting labelling performances were compared to that of a conventional powerful supervised classifier using manually labelled ground-truth data. The supervised classifier results were calculated as 3.5% and 95% for the same case. The results in this series in comparison with the benchmark classifier, suggest that quasi-supervised image texture labelling may be a useful method in the analysis and classification of pathological slides but further study is required to improve the results.

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Section: Quasi-supervised Learningmentioning

confidence: 99%

“…The reasoning and the verification of the cost functional were explained in greater detail in Karacali (2010).…”

Section: Quasi-supervised Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning

Onder¹,

Sarıoğlu²,

Karaçali³

2013

Micron

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show abstract

“…Recently, the quasi-supervised learning method was proposed to address the issue of learning on overlapping datasets that arise in applications where obtaining manually curated ground truth training datasets is problematic and the available labelings are unreliable [14]. Note that in classical pattern classification problems with clear class definitions, the overlap of the datasets associated with different classes reflects an inadequacy of the collected features to present clearly separable regions in the observation space for the respective classes.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for large-scale quasi-supervised learning

Karaçali

2014

Pattern Anal Applic

Self Cite

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We present a novel formulation for quasisupervised learning that extends the learning paradigm to large datasets. Quasi-supervised learning computes the posterior probabilities of overlapping datasets at each sample and labels those that are highly specific to their respective datasets. The proposed formulation partitions the data into sample groups to compute the dataset posterior probabilities in a smaller computational complexity. In experiments on synthetic as well as real datasets, the proposed algorithm attained significant reduction in the computation time for similar recognition performances compared to the original algorithm, effectively generalizing the quasi-supervised learning paradigm to applications characterized by very large datasets.

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SWIFT—scalable clustering for automated identification of rare cell populations in large, high‐dimensional flow cytometry datasets, Part 2: Biological evaluation

et al. 2014

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A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, then splitting and merging stages use a criterion of unimodality to optimize the detection of rare subpopulations, to converge on a consistent cluster number, and to describe non-Gaussian distributions. Probabilistic assignment of cells to clusters, visualization, and manipulation of clusters by their cluster medians, facilitate application of expert knowledge using standard flow cytometry programs. The dual problems of rigorously comparing similar complex samples, and enumerating absent or very rare cell subpopulations in negative controls, were solved by assigning cells in multiple samples to a cluster template derived from a single or combined sample. Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells. A sensitivity of better than one part per million was attained in very large samples. Results were highly consistent on biological replicates, yet the analysis was sensitive enough to show that multiple samples from the same subject were more similar than samples from different subjects. A companion manuscript (Part 1) details the algorithmic development of SWIFT. © 2014 The Authors. Published by Wiley Periodicals Inc.

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Quasi-supervised learning for biomedical data analysis

Cited by 17 publications

References 25 publications

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning

An efficient algorithm for large-scale quasi-supervised learning

SWIFT—scalable clustering for automated identification of rare cell populations in large, high‐dimensional flow cytometry datasets, Part 2: Biological evaluation

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