Distribution-based Low-rank Embedding (2312.17579v1)
Abstract: The early detection of breast abnormalities is a matter of critical significance. Notably, infrared thermography has emerged as a valuable tool in breast cancer screening and clinical breast examination (CBE). Measuring heterogeneous thermal patterns is the key to incorporating computational dynamic thermography, which can be achieved by matrix factorization techniques. These approaches focus on extracting the predominant thermal patterns from the entire thermal sequence. Yet, the task of singling out the dominant image that effectively represents the prevailing temporal changes remains a challenging pursuit within the field of computational thermography. In this context, we propose applying James-Stein for eigenvector (JSE) and Weibull embedding approaches, as two novel strategies in response to this challenge. The primary objective is to create a low-dimensional (LD) representation of the thermal data stream. This LD approximation serves as the foundation for extracting thermomics and training a classification model with optimized hyperparameters, for early breast cancer detection. Furthermore, we conduct a comparative analysis of various embedding adjuncts to matrix factorization methods. The results of the proposed method indicate an enhancement in the projection of the predominant basis vector, yielding classification accuracy of 81.7% (+/-5.2%) using Weibull embedding, which outperformed other embedding approaches we proposed previously. In comparison analysis, Sparse PCT and Deep SemiNMF showed the highest accuracies having 80.9% and 78.6%, respectively. These findings suggest that JSE and Weibull embedding techniques substantially help preserve crucial thermal patterns as a biomarker leading to improved CBE and enabling the very early detection of breast cancer.
- R. L. Siegel, K. D. Miller, H. E. Fuchs, A. Jemal et al., “Cancer statistics, 2021,” Ca Cancer J Clin, vol. 71, no. 1, pp. 7–33, 2021.
- B. Yousefi, H. Akbari, and X. P. Maldague, “Detecting vasodilation as potential diagnostic biomarker in breast cancer using deep learning-driven thermomics,” Biosensors, vol. 10, no. 11, p. 164, 2020.
- P. Gamagami, “Indirect signs of breast cancer: Angiogenesis study,” Atlas of mammography. Cambridge, Mass: Blackwell Science, pp. 321–6, 1996.
- T. Yahara, T. Koga, S. Yoshida, S. Nakagawa, H. Deguchi, and K. Shirouzu, “Relationship between microvessel density and thermographic hot areas in breast cancer,” Surgery today, vol. 33, pp. 243–248, 2003.
- N. Rajic, “Principal component thermography for flaw contrast enhancement and flaw depth characterisation in composite structures,” Composite structures, vol. 58, no. 4, pp. 521–528, 2002.
- R. Usamentiaga, Y. Mokhtari, C. Ibarra-Castanedo, M. Klein, M. Genest, and X. Maldague, “Automated dynamic inspection using active infrared thermography,” IEEE Transactions on Industrial Informatics, vol. 14, no. 12, pp. 5648–5657, 2018.
- S. Marinetti, L. Finesso, and E. Marsilio, “Matrix factorization methods: Application to thermal ndt/e,” NDT & E International, vol. 39, no. 8, pp. 611–616, 2006.
- K. E. Cramer and W. P. Winfree, “Fixed eigenvector analysis of thermographic nde data,” in Thermosense: Thermal Infrared Applications XXXIII, vol. 8013. SPIE, 2011, pp. 225–235.
- B. Yousefi, H. Memarzadeh Sharifipour, M. Eskandari, C. Ibarra-Castanedo, D. Laurendeau, R. Watts, M. Klein, and X. P. Maldague, “Incremental low rank noise reduction for robust infrared tracking of body temperature during medical imaging,” Electronics, vol. 8, no. 11, p. 1301, 2019.
- J. Ahmed, B. Gao, and W. L. Woo, “Wavelet-integrated alternating sparse dictionary matrix decomposition in thermal imaging cfrp defect detection,” IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4033–4043, 2018.
- M. Barry, M. Khansari, P. D. Tallon, C. Carino, M. Hershman, H. C. Fernandes, O. H. Maghsoudi, X. P. V. Maldague, and B. Yousefi, “Multimodal radiothermomic biomarkers for breast cancer screening,” in Thermosense: Thermal Infrared Applications XLIV, A. Mendioroz and N. P. Avdelidis, Eds., vol. 12109, International Society for Optics and Photonics. SPIE, 2022, p. 121090F. [Online]. Available: https://doi.org/10.1117/12.2631751
- B. Yousefi, S. Sfarra, C. I. Castanedo, and X. P. Maldague, “Comparative analysis on thermal non-destructive testing imagery applying candid covariance-free incremental principal component thermography (ccipct),” Infrared Physics & Technology, vol. 85, pp. 163–169, 2017.
- B. Yousefi, S. Sfarra, F. Sarasini, C. I. Castanedo, and X. P. Maldague, “Low-rank sparse principal component thermography (sparse-pct): Comparative assessment on detection of subsurface defects,” Infrared Physics & Technology, vol. 98, pp. 278–284, 2019.
- J.-Y. Wu, S. Sfarra, and Y. Yao, “Sparse principal component thermography for subsurface defect detection in composite products,” IEEE transactions on industrial informatics, vol. 14, no. 12, pp. 5594–5600, 2018.
- C. H. Ding, T. Li, and M. I. Jordan, “Convex and semi-nonnegative matrix factorizations,” IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 1, pp. 45–55, 2008.
- B. Yousefi, S. Sfarra, C. Ibarra-Castanedo, N. P. Avdelidis, and X. P. Maldague, “Thermography data fusion and nonnegative matrix factorization for the evaluation of cultural heritage objects and buildings,” Journal of Thermal Analysis and Calorimetry, vol. 136, pp. 943–955, 2019.
- B. Yousefi, C. Ibarra-Castanedo, and X. P. Maldague, “Infrared non-destructive testing via semi-nonnegative matrix factorization,” Multidisciplinary Digital Publishing Institute Proceedings, vol. 27, no. 1, p. 13, 2019.
- B. Yousefi, C. I. Castanedo, and X. P. Maldague, “Measuring heterogeneous thermal patterns in infrared-based diagnostic systems using sparse low-rank matrix approximation: Comparative study,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–9, 2020.
- B. Yousefi, C. I. Castanedo, and X. P. Maldague, “Low-rank convex/sparse thermal matrix approximation for infrared-based diagnostic system,” arXiv preprint arXiv:2010.06784, 2020.
- B. Yousefi, H. M. Sharifipour, and X. P. Maldague, “Embedded deep regularized block hsic thermomics for early diagnosis of breast cancer,” arXiv preprint arXiv:2106.02106, 2021.
- B. Yousefi, M. Hershman, H. C. Fernandes, and X. P. Maldague, “Concentrated thermomics for early diagnosis of breast cancer,” Engineering Proceedings, vol. 8, no. 1, p. 30, 2021.
- L. R. Goldberg and A. N. Kercheval, “James–stein for the leading eigenvector,” Proceedings of the National Academy of Sciences, vol. 120, no. 2, p. e2207046120, 2023.
- L. R. Goldberg, A. Papanicolaou, and A. Shkolnik, “The dispersion bias,” SIAM Journal on Financial Mathematics, vol. 13, no. 2, pp. 521–550, 2022.
- A. Shkolnik, “James-stein shrinkage for principal components,” Statistics, 2021.
- N. Vigil, B. M. Nouri, H. C. Fernandes, C. Ibarra-Castanedo, X. P. Maldague, and B. Yousefi, “Convex factorization embedding thermography for breast cancer diagnostic,” IEEE Open Journal of Instrumentation and Measurement, vol. 1, pp. 1–8, 2022.
- A. Kızılersü, M. Kreer, and A. W. Thomas, “The weibull distribution,” Significance, vol. 15, no. 2, pp. 10–11, 2018.
- C. Stein, “Inadmissibility of the usual estimator for the mean of a multivariate normal distribution,” in Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics, vol. 3. University of California Press, 1956, pp. 197–207.
- W. James and C. Stein, “Estimation with quadratic loss,” in Breakthroughs in statistics: Foundations and basic theory. Springer, 1992, pp. 443–460.
- L. Silva, D. Saade, G. Sequeiros, A. Silva, A. Paiva, R. Bravo, and A. Conci, “A new database for breast research with infrared image,” Journal of Medical Imaging and Health Informatics, vol. 4, no. 1, pp. 92–100, 2014.
- J. J. Van Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, R. G. Beets-Tan, J.-C. Fillion-Robin, S. Pieper, and H. J. Aerts, “Computational radiomics system to decode the radiographic phenotype,” Cancer research, vol. 77, no. 21, pp. e104–e107, 2017.
- V. Berisha, C. Krantsevich, P. R. Hahn, S. Hahn, G. Dasarathy, P. Turaga, and J. Liss, “Digital medicine and the curse of dimensionality,” NPJ digital medicine, vol. 4, no. 1, p. 153, 2021.
- L. D. Buitrago,J. J. Azarnoosh, X. P. Maldague, and B. Yousefi, “Optimal thermomic biomarkers for early diagnosis of breast cancer,” in Thermosense: Thermal Infrared Applications XLV, vol. 12536. SPIE, 2023, pp. 245–254.
- B. Yousefi, X. P. Maldague, and F. Hassanipour, “Optimal thermomic biomarkers for early diagnosis of breast cancer,” in Engineering Proceedings, vol. 51. AITA, 2023, no. 1 p. 38.