High Throughput Analysis of Breast Cancer Specimens on the Grid

Yang, Lin; Wen-jin, Chen; Meer, Peter; Salaru, Gratian; Feldman, Michael D.; Foran, David J.

doi:10.1007/978-3-540-75757-3_75

Cited by 28 publications

(20 citation statements)

References 16 publications

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“…The second U.S. study (observational, n = 3,744) was also concerned with the efficiency of breast cancer detection, using a grid-enabled computer-assisted diagnosis (CADx) system. 111 As background to the study, the authors pointed out the high prevalence rate of breast cancer in the U.S. for women (about one in eight), whereas death rates from breast cancer continue to decline due to early detection and improved treatment. They …”

Section: Teleradiology Intermediate Outcomesmentioning

confidence: 99%

The Empirical Foundations of Teleradiology and Related Applications: A Review of the Evidence

Bashshur

Krupiński

Thrall

et al. 2016

Telemedicine and e-Health

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Introduction: Radiology was founded on a technological discovery by Wilhelm Roentgen in 1895. Teleradiology also had its roots in technology dating back to 1947 with the successful transmission of radiographic images through telephone lines. Diagnostic radiology has become the eye of medicine in terms of diagnosing and treating injury and disease. This article documents the empirical foundations of teleradiology. Methods: A selective review of the credible literature during the past decade (2005-2015) was conducted, using robust research design and adequate sample size as criteria for inclusion. Findings: The evidence regarding feasibility of teleradiology and related information technology applications has been well documented for several decades. The majority of studies focused on intermediate outcomes, as indicated by comparability between teleradiology and conventional radiology. A consistent trend of concordance between the two modalities was observed in terms of diagnostic accuracy and reliability. Additional benefits include reductions in patient transfer, rehospitalization, and length of stay.

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Section: Teleradiology Intermediate Outcomesmentioning

confidence: 99%

The Empirical Foundations of Teleradiology and Related Applications: A Review of the Evidence

Bashshur

Krupiński

Thrall

et al. 2016

Telemedicine and e-Health

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“…These labels are indispensable for the training of classifiers, for their validation and it is highly non-trivial to acquire these labels from a technical as well as statistical point. Although studies were conducted on a global estimation of staining on TMA spots [12,13], to our knowledge this is the first in depth study for tissue microarrays which incorporates expert labeling information down to the detail and precision of single cell nuclei.…”

Section: Generating a Gold Standardmentioning

confidence: 99%

Randomized Tree Ensembles for Object Detection in Computational Pathology

Fuchs

Haybaeck²,

Wild

et al. 2009

Advances in Visual Computing

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Randomized tree ensembles for object detection in computational pathology AbstractModern pathology broadly searches for biomarkers which are predictive for the survival of patients or the progression of cancer. Due to the lack of robust analysis algorithms this work is still performed manually by estimating staining on whole slides or tissue microarrays (TMA). Therefore, the design of decision support systems which can automate cancer diagnosis as well as objectify it pose a highly challenging problem for the medical imaging community. In this paper we propose Relational Detection Forests (RDF) as a novel object detection algorithm, which can be applied in an off-the-shelf manner to a large variety of tasks. The contributions of this work are twofold: (i) we describe a feature set which is able to capture shape information as well as local context. Furthermore, the feature set is guaranteed to be generally applicable due to its high flexibility.(ii) we present an ensemble learning algorithm based on randomized trees, which can cope with exceptionally high dimensional feature spaces in an efficient manner. Contrary to classical approaches, subspaces are not split based on thresholds but by learning relations between features. The algorithm is validated on tissue from 133 human clear cell renal cell carcinoma patients (ccRCC) and on murine liver samples of eight mice. On both species RDFs compared favorably to state of the art methods and approaches the detection accuracy of trained pathologists. Abstract. Modern pathology broadly searches for biomarkers which are predictive for the survival of patients or the progression of cancer. Due to the lack of robust analysis algorithms this work is still performed manually by estimating staining on whole slides or tissue microarrays (TMA). Therefore, the design of decision support systems which can automate cancer diagnosis as well as objectify it pose a highly challenging problem for the medical imaging community. In this paper we propose Relational Detection Forests (RDF) as a novel object detection algorithm, which can be applied in an off-the-shelf manner to a large variety of tasks. The contributions of this work are twofold: (i) we describe a feature set which is able to capture shape information as well as local context. Furthermore, the feature set is guaranteed to be generally applicable due to its high flexibility. (ii) we present an ensemble learning algorithm based on randomized trees, which can cope with exceptionally high dimensional feature spaces in an efficient manner. Contrary to classical approaches, subspaces are not split based on thresholds but by learning relations between features. The algorithm is validated on tissue from 133 human clear cell renal cell carcinoma patients (ccRCC) and on murine liver samples of eight mice. On both species RDFs compared favorably to state of the art methods and approaches the detection accuracy of trained pathologists. Randomized Tree Ensembles for Object Detection in Computational Pathology

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“…However, we have collected benchmarks from a small number of studies [6,21]. They are summarised in Tables 6 and 7 and compared with our results.…”

Section: Discussionmentioning

confidence: 99%

Ultra-fast processing of gigapixel Tissue MicroArray images using High Performance Computing

Wang

McCleary²,

Wang³

et al. 2011

Cell Oncol.

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Abstract. Background: Tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.Methods: A High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.Results: The automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.Conclusions: The methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research.

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High Throughput Analysis of Breast Cancer Specimens on the Grid

Cited by 28 publications

References 16 publications

The Empirical Foundations of Teleradiology and Related Applications: A Review of the Evidence

The Empirical Foundations of Teleradiology and Related Applications: A Review of the Evidence

Randomized Tree Ensembles for Object Detection in Computational Pathology

Ultra-fast processing of gigapixel Tissue MicroArray images using High Performance Computing

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