Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation

Chung, Yu-An; Yang, Shao-Wen; Lin, Hung‐Mo

doi:10.1109/taai51410.2020.00028

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…Of note, SSL allows representation learning independently from the grade prediction task (SimCLR 33 ). NN2 and NN3 are trained with a cost‐aware classification loss 34 to take advantage of the ordinal nature of the classes. The model was trained to estimate class‐specific risk, as opposed to posterior probability, with risks determined by a cost matrix (Table S2) that penalizes large errors.…”

Section: Methodsmentioning

confidence: 99%

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

Lubrano,

Bellahsen‐Harrar,

Berlemont

et al. 2023

Histopathology

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BackgroundDiagnosis of head and neck (HN) squamous dysplasias and carcinomas is critical for patient care, cure, and follow‐up. It can be challenging, especially for grading intraepithelial lesions. Despite recent simplification in the last WHO grading system, the inter‐ and intraobserver variability remains substantial, particularly for nonspecialized pathologists, exhibiting the need for new tools to support pathologists.MethodsIn this study we investigated the potential of deep learning to assist the pathologist with automatic and reliable classification of HN lesions following the 2022 WHO classification system. We created, for the first time, a large‐scale database of histological samples (>2000 slides) intended for developing an automatic diagnostic tool. We developed and trained a weakly supervised model performing classification from whole‐slide images (WSI). We evaluated our model on both internal and external test sets and we defined and validated a new confidence score to assess the predictions that can be used to identify difficult cases.ResultsOur model demonstrated high classification accuracy across all lesion types on both internal and external test sets (respectively average area under the curve [AUC]: 0.878 (95% confidence interval [CI]: [0.834–0.918]) and 0.886 (95% CI: [0.813–0.947])) and the confidence score allowed for accurate differentiation between reliable and uncertain predictions.ConclusionOur results demonstrate that the model, associated with confidence measurements, can help in the difficult task of classifying HN squamous lesions by limiting variability and detecting ambiguous cases, taking us one step closer to a wider adoption of AI‐based assistive tools.

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Section: Methodsmentioning

confidence: 99%

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

Lubrano,

Bellahsen‐Harrar,

Berlemont

et al. 2023

Histopathology

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“…Cost-sensitive multiclass classification has already been applied to other fields [7][8][9][10][11][12]. However, in this section, we present summaries of some of the more recent works.…”

Section: Related Workmentioning

confidence: 99%

Framework for Benefit-Based Multiclass Classification of Diseases

Sooklal,

Hosein

2024

Preprint

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Purpose: Health datasets typically comprise of data that are heavily skewed towards the healthy class, thus resulting in classifiers erring towards this majority class. Due to this imbalance of data, traditional performance metrics, such as accuracy, are not appropriate for evaluating the performance of classifiers with the minority class (disease-affected). In addition, classifiers are trained under the assumption that the costs or benefits associated with different decision outcomes are equal. However, this is usually not the case with health data since there are different benefits/costs associated with the correct/incorrect identification of disease affected/unhealthy persons rather than healthy individuals. In this paper we address these problems by examining benefits/costs both when training and evaluating the performance of classifiers. Furthermore,we focus on multiclass classification where the outcome can be one of three or more options. Methods: We propose modifications to the Naive Bayes and Logistic Regression algorithms to incorporate costs and benefits when training for the multiclass scenario, as well as compare these to a recently proposed algorithm in the field, hierarchical cost-sensitive kernel logistic regression, and also an adapted hierarchical approach with our cost-benefit based logistic regression model. Wedemonstrate the effectiveness of all approaches for fetal health classification, vertebral column classification and hepatitis C/fibrosis/cirrhosis prediction. Results: Our proposed multiclass Logistic Regression algorithm outperformed all other algorithms, improving performance with the more critical classes. Conclusion: Our proposed multiclass Logistic Regression algorithm is robust and suitable for cases where costs and benefits of the various decision outcomes are important.

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Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation

Cited by 2 publications

References 38 publications

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

Framework for Benefit-Based Multiclass Classification of Diseases

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