Towards objective and reproducible study of patient-doctor interaction: Automatic text analysis based VR-CoDES annotation of consultation transcripts

Birkett, Charlotte; Arandjelović, Ognjen; Humphris, Gerry

doi:10.1109/embc.2017.8037399

Cited by 14 publications

(5 citation statements)

References 21 publications

(25 reference statements)

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“…Since each WSI has different numbers of tiles from a given cluster, patch level predictions are represented by normalized histograms, thus effecting a homogeneous representation. A support vector machine (SVM) classifier [5] is trained to learn the cluster level outcome.…”

Section: Aggregation Of Predictionsmentioning

confidence: 99%

Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Yue¹,

Dimitriou

Arandjelović

EPiC Series in Computing

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Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics. Owing to its potential for major public health impact, in recent years DP has been attracting much research attention. Nevertheless, a wide breadth of significant conceptual and technical challenges remain, few of them greater than those encountered in the field of oncology. The automatic analysis of digital pathology slides of cancerous tissues is particularly problematic due to the inherent heterogeneity of the disease, extremely large images, amongst numerous others. In this paper we introduce a novel machine learning based framework for the prediction of colorectal cancer outcome from whole digitized haematoxylin & eosin (H&E) stained histopathology slides. Using a real-world data set we demonstrate the effectiveness of the method and present a detailed analysis of its different elements which corroborate its ability to extract and learn salient, discriminative, and clinically meaningful content.

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Section: Aggregation Of Predictionsmentioning

confidence: 99%

Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Yue¹,

Dimitriou

Arandjelović

EPiC Series in Computing

Self Cite

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“…Lastly, we turn our attention to the AUROC, i.e., the area under the receiver operating characteristic curve, which is used extensively in medical research as a means of ranking different diagnostic procedures [12][13][14][15][16][17]. Recall that the receiver operator characteristic (ROC) curve captures the dependency between of the true positive rate on the the false positive rate of a binary classifier.…”

Section: Performance Quantificationmentioning

confidence: 99%

Nuances of Interpreting X-ray Analysis by Deep Learning and Lessons for Reporting Experimental Findings

Valsson

Arandjelović

2022

Sci

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With the increase in the availability of annotated X-ray image data, there has been an accompanying and consequent increase in research on machine-learning-based, and ion particular deep-learning-based, X-ray image analysis. A major problem with this body of work lies in how newly proposed algorithms are evaluated. Usually, comparative analysis is reduced to the presentation of a single metric, often the area under the receiver operating characteristic curve (AUROC), which does not provide much clinical value or insight and thus fails to communicate the applicability of proposed models. In the present paper, we address this limitation of previous work by presenting a thorough analysis of a state-of-the-art learning approach and hence illuminate various weaknesses of similar algorithms in the literature, which have not yet been fully acknowledged and appreciated. Our analysis was performed on the ChestX-ray14 dataset, which has 14 lung disease labels and metainfo such as patient age, gender, and the relative X-ray direction. We examined the diagnostic significance of different metrics used in the literature including those proposed by the International Medical Device Regulators Forum, and present the qualitative assessment of the spatial information learned by the model. We show that models that have very similar AUROCs can exhibit widely differing clinical applicability. As a result, our work demonstrates the importance of detailed reporting and analysis of the performance of machine-learning approaches in this field, which is crucial both for progress in the field and the adoption of such models in practice.

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“…Ensemble approach provides 97.42% accuracy after using feature selection and other reprocessing techniques on the breast cancer dataset. 16 Figure 1 shows aspects related to treatment of breast cancer.…”

Section: F I G U R E 1 Aspects Related To Treatment Of Breast Cancermentioning

confidence: 99%

Feature selection and classification using support vector machine and decision tree

Durgalakshmi

Vijayakumar

2020

Computational Intelligence

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Breast cancer is one of the human threats which cause morbidity and mortality worldwide. The death rate can be reduced by advanced diagnosis. The objective of this article is to select the reduced number of features the help in diagnosing breast cancer in Wisconsin Diagnostic Breast Cancer (WDBC). This proposed model depicts women who all have no cancer cells or in benign stage later develop into malignant (metastases). Due to the dynamic nature of the big data framework, the proposed method ensures high confidence and low execution time. Moreover, healthcare information growth chases an exponential pattern, and current database systems cannot adequately manage the massive amount of data. So, it is requisite to adopt the "big data" solution for healthcare information.

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Towards objective and reproducible study of patient-doctor interaction: Automatic text analysis based VR-CoDES annotation of consultation transcripts

Cited by 14 publications

References 21 publications

Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Nuances of Interpreting X-ray Analysis by Deep Learning and Lessons for Reporting Experimental Findings

Feature selection and classification using support vector machine and decision tree

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