ArticleIEEE transactions on medical imaging2020
Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models.
Article in IEEE transactions on medical imaging, 2020. 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.
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Enhanced Collaborative Edge Intelligence for Explainable and Transferable Image Recognition in 6G-Aided IIoT.Sensors (Basel, Switzerland) · 2025Article
- Precision measurement of stratum corneum thickness in OCT images using variational autoencoders and advanced DSP techniques.Frontiers in bioengineering and biotechnology · 2025Review
- Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency.Journal of pathology informatics · 2024Article
- SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects.Medical image analysis · 2024Article
- Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases.Journal of medical systems · 2024Article
- CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy.IEEE transactions on medical imaging · 2024Article
- Bibliometric analysis for artificial intelligence in the internet of medical things: mapping and performance analysis.Frontiers in artificial intelligence · 2024Review
- Article
- MR-based spatiotemporal anisotropic atrophy evaluation of hippocampus in Alzheimer's disease progression by multiscale skeletal representation.Human brain mapping · 2023Article
- Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023Article
- WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections.Computer methods and programs in biomedicine · 2022Article
- Hippocampal representations for deep learning on Alzheimer's disease.Scientific reports · 2022Article
- Interpretable cardiac anatomy modeling using variational mesh autoencoders.Frontiers in cardiovascular medicine · 2022Article
- Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology.Frontiers in physiology · 2022Article
- Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer's disease.Psychoradiology · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Explainable Deep Learning Models in Medical Image Analysis.Journal of imaging · 2020Review
- Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy.Frontiers in cardiovascular medicine · 2020Review
Corrections and comments
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Authors and funding
15 authors.
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Abstract
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging.
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