ArticleProceedings of SPIE--the International Society for Optical Engineering2018
Deep learning for biomarker regression: application to osteoporosis and emphysema on chest CT scans.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Deep Learning for Osteoporosis Diagnosis Using Magnetic Resonance Images of Lumbar Vertebrae.Journal of imaging informatics in medicine · 2026Article
- Emphysema progression risk in COPD using a localized foundational model of density evolution.NPJ digital medicine · 2025Article
- Novel Lobe-based Transformer model (LobTe) to predict emphysema progression in Alpha-1 Antitrypsin Deficiency.Computers in biology and medicine · 2025Article
- Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol.PloS one · 2025Article
- Severe chest and back pain due to extra-pulmonary emphysema above 5,000 m: a series of case reports.Frontiers in medicine · 2025Article
- Assessment of submicron bone tissue composition in plastic-embedded samples using optical photothermal infrared (O-PTIR) spectral imaging and machine learning.Journal of structural biology: X · 2024Article
- Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts.Scientific reports · 2024Article
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review.Archives of computational methods in engineering : state of the art reviews · 2023Review
- COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.The British journal of radiology · 2022Article
- Automatic coronary artery calcium scoring on routine chest computed tomography (CT): comparison of a deep learning algorithm and a dedicated calcium scoring CT.Quantitative imaging in medicine and surgery · 2022Article
- Applications of Machine Learning in Bone and Mineral Research.Endocrinology and metabolism (Seoul, Korea) · 2021Review
- Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning.Nature communications · 2021Article
- Deep learning in systems medicine.Briefings in bioinformatics · 2021Review
- Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates.Biomolecules · 2020Article
- ICU admission body composition: skeletal muscle, bone, and fat effects on mortality and disability at hospital discharge-a prospective, cohort study.Critical care (London, England) · 2020Observational
- Biomarker Localization From Deep Learning Regression Networks.IEEE transactions on medical imaging · 2020Article
- Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks.Scientific reports · 2020Article
- Artificial intelligence, osteoporosis and fragility fractures.Current opinion in rheumatology · 2019Review
- LOCALIZING IMAGE-BASED BIOMARKER REGRESSION WITHOUT TRAINING MASKS: A NEW APPROACH TO BIOMARKER DISCOVERY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
introductionBiomarker computation using deep-learning often relies on a two-step process, where the deep learning algorithm segments the region of interest and then the biomarker is measured. We propose an alternative paradigm, where the biomarker is estimated directly using a regression network. We showcase this image-to-biomarker paradigm using two biomarkers: the estimation of bone mineral density (BMD) and the estimation of lung percentage of emphysema from CT scans. MATERIALS AND
methodsWe use a large database of 9,925 CT scans to train, validate and test the network for which reference standard BMD and percentage emphysema have been already computed. First, the 3D dataset is reduced to a set of canonical 2D slices where the organ of interest is visible (either spine for BMD or lungs for emphysema). This data reduction is performed using an automatic object detector. Second, The regression neural network is composed of three convolutional layers, followed by a fully connected and an output layer. The network is optimized using a momentum optimizer with an exponential decay rate, using the root mean squared error as cost function.
resultsThe Pearson correlation coefficients obtained against the reference standards are
conclusionsThe deep-learning regression architecture can learn biomarkers from images directly, without indicating the structures of interest. This approach simplifies the development of biomarker extraction algorithms. The proposed data reduction based on object detectors conveys enough information to compute the biomarkers of interest.
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