ArticleDiagnostics (Basel, Switzerland)2022
A Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.BMC cancer · 2025Pooled it
- Testing Transferability of a Mortality Risk Model for Interhospital Transfer Using Real-World Electronic Health Records: External Validation and Model Enhancement.Critical care explorations · 2026Article
- Breast cancer detection and classification via a robust deep learning approach.Scientific reports · 2026Article
- ADC Histogram Features of Breast Cancer Brain Metastases as Candidate Imaging Biomarkers of Primary Tumor ER, PR, Ki-67, and Luminal Status.Diagnostics (Basel, Switzerland) · 2026Article
- [Enhancement of radiomics-based machine learning models for predicting efficacy of high-intensity focused ultrasound ablation of uterine fibroids using undersampling methods].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Over-detection and over-surveillance in breast screening: current status and the potential for artificial intelligence optimisation.Insights into imaging · 2025Review
- Neural Network-Based Mammography Analysis: Augmentation Techniques for Enhanced Cancer Diagnosis-A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Linear B-cell epitope prediction for SARS and COVID-19 vaccine design: Integrating balanced ensemble learning models and resampling strategies.PeerJ. Computer science · 2025Article
- Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes.PeerJ. Computer science · 2025Article
- LesionSCynth: A simple parametric lesion synthesis method to improve spinal cord lesion segmentation in low-data scenarios.Imaging neuroscience (Cambridge, Mass.) · 2025Article
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- Generating Synthetic Data for Medical Imaging.Radiology · 2024Review
- Improving the performance of deep learning models in predicting and classifying gamma passing rates with discriminative features and a class balancing technique: a retrospective cohort study.Radiation oncology (London, England) · 2024Article
- Identifying High-Quality Leads among Screened Anticancerous Compounds Using SMILES Representations.ACS omega · 2024Article
- Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Attention-Based Deep Learning System for Classification of Breast Lesions-Multimodal, Weakly Supervised Approach.Cancers · 2023Article
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2 authors.
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Abstract
Tools based on deep learning models have been created in recent years to aid radiologists in the diagnosis of breast cancer from mammograms. However, the datasets used to train these models may suffer from class imbalance, i.e., there are often fewer malignant samples than benign or healthy cases, which can bias the model towards the healthy class. In this study, we systematically evaluate several popular techniques to deal with this class imbalance, namely, class weighting, over-sampling, and under-sampling, as well as a synthetic lesion generation approach to increase the number of malignant samples. These techniques are applied when training on three diverse Full-Field Digital Mammography datasets, and tested on in-distribution and out-of-distribution samples. The experiments show that a greater imbalance is associated with a greater bias towards the majority class, which can be counteracted by any of the standard class imbalance techniques. On the other hand, these methods provide no benefit to model performance with respect to Area Under the Curve of the Recall Operating Characteristic (AUC-ROC), and indeed under-sampling leads to a reduction of 0.066 in AUC in the case of a 19:1 benign to malignant imbalance. Our synthetic lesion methodology leads to better performance in most cases, with increases of up to 0.07 in AUC on out-of-distribution test sets over the next best experiment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.