Privacy Policy and Technology in Biomedical Data Science

Arellano, April Moreno; Dai, Wenrui; Wang, Shuang; Jiang, Xiaoqian; Ohno-Machado, Lucila

doi:10.1146/annurev-biodatasci-080917-013416

Cited by 40 publications

(40 citation statements)

References 85 publications

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“…In the United States (US), research must follow the Federal Policy for the Protection of Human Subjects (the "Common Rule") (Arellano, Dai, Wang, Jiang, & Ohno-Machado, 2018). This requires that specific consent be obtained from participants before their data and/or specimens can be used not only for the research project for which they are enrolling, but also for future research that may use such material, which often is the case of ENIGMA projects.…”

Section: Data Sharing and Re-usementioning

confidence: 99%

Mega-Analysis Methods in ENIGMA: The Experience of the Generalized Anxiety Disorder Working Group

Zugman¹,

Harrewijn²,

Cardinale³

et al. 2020

Preprint

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The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.

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Section: Data Sharing and Re-usementioning

confidence: 99%

Mega-Analysis Methods in ENIGMA: The Experience of the Generalized Anxiety Disorder Working Group

Zugman¹,

Harrewijn²,

Cardinale³

et al. 2020

Preprint

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“…Limited by the reality of strict privacy policies and nonuniform medical management systems, most medical samples are inaccessible, especially labelled samples [ 34 , 35 ]. Hence, transfer learning techniques were employed to train the EfficientNet-B5 network given our relatively moderate training dataset.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study

Yang

Chen

Cheng

et al. 2021

BMC Med

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Background Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs. Methods We initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People’s Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model. Results We developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. Conclusions Multi-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios.

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“…Issues related to institutional regulations are especially challenging for international collaborations, as policies differ widely by country (Arellano et al, 2018;Chassang, 2017;Dove, 2018). Effective coordination of multi-site studies is especially challenging without dedicated personnel and administrative resources.…”

Section: Multi-site Studiesmentioning

confidence: 99%

Open Science in Psychophysiology: An Overview of Challenges and Emerging Solutions

Garrett-Ruffin¹,

Hindash²,

Kaczkurkin³

et al. 2020

Preprint

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The present review is the result of a one-day workshop on open science, held at the Annual Meeting of the Society for Psychophysiological Research in Washington, DC, September 2019. The contributors represent psychophysiological researchers at different career stages and from a wide spectrum of institutions. We discuss the state of open science in psychophysiology as perceived from different perspectives, define key challenges, and propose a set of measures that are intended to facilitate open science practices.

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Privacy Policy and Technology in Biomedical Data Science

Cited by 40 publications

References 85 publications

Mega-Analysis Methods in ENIGMA: The Experience of the Generalized Anxiety Disorder Working Group

Mega-Analysis Methods in ENIGMA: The Experience of the Generalized Anxiety Disorder Working Group

Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study

Open Science in Psychophysiology: An Overview of Challenges and Emerging Solutions

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