Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

Study proposed algorithm. by Ainhoa Pérez-Guerrero (21377457)

Subjects: “…assess microvascular function…”

Continuous Probability Distributions generated by the PIPE Algorithm by LUIS G.B. PINHO (14073372)

“…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …”

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Generated blended data using mouse brain cells and human embryonic heart cells, labeled as <i>Mix</i>[<i>a</i>,<i>b</i>,<i>c</i>], indicates the proportion of mouse brain cells fou... by Nuray Sogunmez Erdogan (21533872)

The expert screening cost <i>g</i>_expert(<i>x</i>) defined for the Challenge: Mean cost for screening a fraction <i>x</i> of a patient cohort for cardiac abnormalities. by Matthew A. Reyna (16965038)

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Demographic, murmur, and clinical outcome information in the Challenge training, validation, and/or test sets; nan values indicate unknown or missing values. by Matthew A. Reyna (16965038)

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Demographic, murmur, and clinical outcome distributions across the Challenge training, validation, and test data. by Matthew A. Reyna (16965038)

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Weighted accuracy metric (1) for the murmur detection task (<i>x</i>-axis) and the cost metric (8) scores for the clinical outcome identification task (<i>y</i>-axis) of the final... by Matthew A. Reyna (16965038)

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Scores of the officially ranked methods on the test set for the clinical outcome identification task. by Matthew A. Reyna (16965038)

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Auscultation locations for the CirCor DigiScope dataset [6], which was used for the Challenge: Pulmonary valve (PV), aortic valve (AV), mitral valve (MV), and tricuspid valve (TV). by Matthew A. Reyna (16965038)

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Traditional evaluation metric (<i>x</i>-axis) and cost metric (<i>y</i>-axis) scores for the final Challenge entries with the clinical outcome identification task on the hidden tes... by Matthew A. Reyna (16965038)

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Traditional evaluation metric (<i>x</i>-axis) and weighted accuracy metric (<i>y</i>-axis) scores for the final Challenge entries with the murmur detection task on the hidden test... by Matthew A. Reyna (16965038)

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Confusion matrix <i>N</i> for clinical outcome detection with two classes: Clinical outcome abnormal and clinical outcome normal. by Matthew A. Reyna (16965038)

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Screening and diagnosis pipeline for the Challenge. by Matthew A. Reyna (16965038)

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Confusion matrix <i>M</i> for murmur detection with three classes: Murmur present, murmur unknown, and murmur absent. by Matthew A. Reyna (16965038)

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Scores of the officially ranked methods on the test set for the murmur detection task. by Matthew A. Reyna (16965038)

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The expert screening cost <i>g</i>_expert(<i>x</i>) defined for the Challenge: Mean cost for screening a fraction <i>x</i> of a patient cohort for cardiac abnormalities. by Matthew A. Reyna (16965038)

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Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”