ArticleFrontiers in artificial intelligence2021
A Modified AUC for Training Convolutional Neural Networks: Taking Confidence Into Account.
Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis.European radiology · 2024Pooled it
- Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.Shock (Augusta, Ga.) · 2026Article
- FT-MDNNMDs: early detection of breast cancer using fine-tuned multi-deep neural networks with TCGA and clinical image datasets.Scientific reports · 2026Article
- Exploring the Potential Value of Inflammatory Response-Associated Genes in Chronic Rhinosinusitis with Nasal Polyps: An Integrated Bioinformatics and Experimental Validation Analysis.Journal of inflammation research · 2026Article
- Refining cancer prediction with DNA sequencing and combined machine learning approaches.Scientific reports · 2025Article
- Smart emergency care: a narrative review of predictive machine learning models.Annals of translational medicine · 2025Review
- Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.BMC medical imaging · 2025Article
- Predicting 30-day and 1-year mortality in heart failure with preserved ejection fraction (HFpEF).PloS one · 2025Article
- SVMVGGNet-16: A Novel Machine and Deep Learning Based Approaches for Lung Cancer Detection Using Combined SVM and VGGNet-16.Current medical imaging · 2025Article
- Investigating the impact of extreme weather events and related indicators on cardiometabolic multimorbidity.Archives of public health = Archives belges de sante publique · 2024Article
- Article
- An investigation into the deep learning approach in sentimental analysis using graph-based theories.PloS one · 2021Article
- Age-Dependent Effects of Homocysteine on Erectile Dysfunction Risk Among U.S. Males: A NHANES Analysis.American journal of men's healthArticle
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3 authors.
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Abstract
Receiver operating characteristic (ROC) curve is an informative tool in binary classification and Area Under ROC Curve (AUC) is a popular metric for reporting performance of binary classifiers. In this paper, first we present a comprehensive review of ROC curve and AUC metric. Next, we propose a modified version of AUC that takes confidence of the model into account and at the same time, incorporates AUC into Binary Cross Entropy (BCE) loss used for training a Convolutional neural Network for classification tasks. We demonstrate this on three datasets: MNIST, prostate MRI, and brain MRI. Furthermore, we have published GenuineAI, a new python library, which provides the functions for conventional AUC and the proposed modified AUC along with metrics including sensitivity, specificity, recall, precision, and F1 for each point of the ROC curve.
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