Global and Local Interpretability for Cardiac MRI Classification
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Clough et al. (2019) reconstructed images with low peak ejection rate by adding the CAV to the latent space.
“Furthermore, they reconstructed images with low peak ejection rate - a characteristic that might be related to cardiac disease - by adding the CAV to the latent space.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Clough et al. (2019)
Clough et al. (2019) identified cardiac disease in cine-MRI by classifying the latent space of a VAE.
“Clough et al. (2019) identified cardiac disease in cine-MRI by classifying the latent space of a VAE.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Clough et al. (2019)
Clough et al. (2019) used TCAV to show which clinically known biomarkers were related to cardiac disease in their model.
“They used TCAV to show which clinically known biomarkers were related to cardiac disease in their model.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Clough et al. (2019)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2743464 · Cited by 751 · see citing works
Flagged first, then by citations
Research works printed but not linked: 13
- [1]1. Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Advances in Neural Information Processing Systems. pp. 9525-9536 (2018)
- [2]2. Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A.M., Aung, N., Lukaschuk, E., Sanghvi, M.M., et al.: Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. Journal of Cardiovascular Magnetic Resonance 20 (1), 65 (2018)
- [3]3. Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P., Cetin, I., Lekadir, K., Camara, O., Ballester, M., Zemrak, F.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE transactions on medical imaging 37 (11), 2514-2525 (2018)
- [4]4. Biffi, C., Oktay, O., Tarroni, G., Bai, W., De Marvao, A., Doumou, G., Rajchl, M., Bedair, R., Prasad, S., Cook, S., et al.: Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 464-471. Springer (2018)
- [5]5. Goodman, B., Flaxman, S.: EU regulations on algorithmic decision-making and a right to explanation. In: ICML workshop on human interpretability in machine learning (WHI 2016), New York, NY. (2016)
- [6]6. Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., Sayres, R.: Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV). arXiv:1711.11279 (2017) · arXiv
- [7]7. Kingma, D., Welling, M.: Auto-Encoding Variational Bayes. arXiv preprint arXiv:1312.6114 (2013) · arXiv
- [9]9. Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., Mordvintsev, A.: The building blocks of interpretability. Distill (2018), https://distill.pub/2018/building-blocks
- [10]10. Patel, V., Gopalan, R., Li, R., Chellappa, R.: Visual domain adaptation: A survey of recent advances. IEEE signal processing magazine 32 (3), 53-69 (2015)
- [11]11. Petersen, S.E., Matthews, P.M., Francis, J.M., Robson, M.D., Zemrak, F., Boubertakh, R., Young, A.A., Hudson, S., Weale, P., Garratt, S., Collins, R., Piechnik, S., Neubauer, S.: UK Biobanks cardiovascular magnetic resonance protocol. Journal of cardiovascular magnetic resonance 18 (1), 8 (2015)
- [12]12. Rudin, C.: Please stop explaining black box models for high stakes decisions. arXiv:1811.10154 (2018) · arXiv
- [14]14. Savitzky, A., Golay, M.J.: Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry 36 (8), 1627-1639 (1964)
- [15]15. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv:1312.6034 (2013) · arXiv
Cited by
Sorted by citations
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328 · see citing works
Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology
Sauter, D., Lodde, G., Nensa, F., Schadendorf, D., Livingstone, E., & Kukuk, M. (2022) · Sensors · doi:10.3390/s22145346 · Cited by 26 · see citing works
- Authors
- Clough, J. R., Oksuz, I., Puyol-Antón, E., Ruijsink, B., King, A. P., & Schnabel, J. A.
- Affiliations
- King's College London (GB)
- Corresponding author
- Clough, James R.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 656-664
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2949476443
- Cited by
- 73
- References
- 1
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 10 · 2023: 17 · 2022: 14 · 2021: 7 · 2020: 5 · 2019: 2
- FWCI
- 2.62
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- Engineering and Physical Sciences Research Council ([WT203148/Z/16/Z], EP/P001009/, EP/P001009/1, 17806, EP/N026993/1, EP/M000133/1, WT 203148/Z/16/, 203148/ Z/16/Z)
- Menzies Centre for Australian Studies, King's College London, University of London (WT 203148/Z/16/Z)
- Centre For Medical Engineering, King’s College London (203148/Z/16/Z, WT 203148/Z/16/Z)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- interpretability, variational autoencoder, concept activation vectors, latent space interpolation, coronary artery disease detection, left ventricular ejection fraction, UK Biobank
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
