ArticleScientific reports2020
Volumetric breast density estimation on MRI using explainable deep learning regression.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging.Sensors (Basel, Switzerland) · 2026Pooled it
- Alzheimer's disease prediction via an explainable CNN using genetic algorithm and SHAP values.PloS one · 2026Article
- Impact of menopause and age on breast density and background parenchymal enhancement in dynamic contrast-enhanced magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue.Scientific reports · 2025Article
- Comparative analysis of deep learning architectures for breast region segmentation with a novel breast boundary proposal.Scientific reports · 2025Article
- Computer-aided cholelithiasis diagnosis using explainable convolutional neural network.Scientific reports · 2025Article
- Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysis.Computational and structural biotechnology journal · 2024Review
- MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.Aesthetic plastic surgery · 2024Article
- Article
- A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI.Scientific reports · 2024Article
- XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer's Disease.IEEE journal of translational engineering in health and medicine · 2024Article
- Three-dimensional deep regression-based light scattering imaging system for nanoscale exosome analysis.Biomedical optics express · 2023Article
- Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans.IEEE transactions on medical imaging · 2023Article
- Article
- Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.Frontiers in medicine · 2023Review
- A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features.Sensors (Basel, Switzerland) · 2022Article
- Deep learning in breast imaging.BJR open · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast density estimation. A 3-dimensional regression convolutional neural network (CNN) was used to estimate the volumetric breast density. Patients were split in training (N = 400), validation (N = 50), and hold-out test set (N = 165). Hyperparameters were optimized using Neural Network Intelligence and augmentations consisted of translations and rotations. The estimated densities were evaluated to the ground truth using Spearman's correlation and Bland-Altman plots. The output of the CNN was visually analyzed using SHapley Additive exPlanations (SHAP). Spearman's correlation between estimated and ground truth density was ρ = 0.81 (N = 165, P < 0.001) in the hold-out test set. The estimated density had a median bias of 0.70% (95% limits of agreement = - 6.8% to 5.0%) to the ground truth. SHAP showed that in correct density estimations, the algorithm based its decision on fibroglandular and fatty tissue. In incorrect estimations, other structures such as the pectoral muscle or the heart were included. To conclude, it is feasible to automatically estimate volumetric breast density on MRI without segmentations, and to provide accompanying explanations.
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Registered trials
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.