ENG
РУС
Search
Menu
About the Edition
Open Access Statement
Editorial Board
Editorial Policy
Editorial Ethics
Rules for peer-review
Rules for presentation of papers
Forms of Submitted Documents
Recommendations for the Design of References to Our Journal
Editorial Staff
Information for Subscribers
Summaries of the Issue
List of Authors
Issues
Publishers license agreement
Procedure of work with personal data
Publications
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
Editor-in-Chief

Nikiforov
Vladimir O.
D.Sc., Prof.
Partners













































doi: 10.17586/2226-1494-2021-21-4-535-544

Bayesian losses for homoscedastic aleatoric uncertainty modeling in pollen image detection

N. E. Khanzhina


Read the full article  ';
Article in Russian

For citation:
Khanzhina N.E. Bayesian losses for homoscedastic aleatoric uncertainty modeling in pollen image detection. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 4, pp. 535–544 (in Russian). doi: 10.17586/2226-1494-2021-21-4-535-544


Abstract
The paper investigates the homoscedastic aleatoric uncertainty modeling for the detection of pollen in images. The new uncertainty modeling loss functions are presented, which are based on the focal and smooth L1 losses. The focal and smooth L1 losses proved their efficiency for the problem of image detection, however, they do not allow modeling the aleatoric uncertainty, while the proposed functions do, leading to more accurate solutions. The functions are based on Bayesian inference and allow for effortless use in existing neural network detectors based on the RetinaNet architecture. The advantages of the loss functions are described on the problem of pollen detection in images. The new loss functions increased the accuracy of pollen image detection, namely localization and classification, on average by 2.76 %, which is crucial for the pollen recognition in general. This helps to automate the process of determining allergenic pollen in the air and reduce the time to inform patients with pollinosis to prevent allergy symptoms. The obtained result shows that the modeling of homoscedastic aleatoric uncertainty for neural networks allows separating the noise from the data, increasing the accuracy of the proposed solutions. The developed functions can be applied to train neural network detectors on any other image datasets.

Keywords: Bayesian deep learning, Bayesian inference, aleatoric uncertainty, pollen recognition, object detection, uncertainty quantification, Bayesian modeling

Acknowledgements. This work is financially supported by National Center for Cognitive Research of ITMO University. The author would like to thank Alexey Lapenok, Natalia Minaeva, Larisa Novoselova, Georgiy Zamorin, Tatyana Polevaya, Andrey Filchenkov, Elena Zamyatina, Irina Kharisova, Yuliya Pinaeva and Evgeny Tsymbalov for their great help and useful comments.

References
  1. Pawankar R., Canonica G.W., Holgate S., Lockey R. WAO White Book on Allergy. USA, World Allergy Organization, 2013, 242 p.
  2. Bousquet P.J., Chinn S., Janson C., Kogevinas M., Burney P., Jarvis D. Geographical variation in the prevalence of positive skin tests to environmental aeroallergens in the European Community Respiratory Health Survey I.Allergy, 2007, vol. 62, no. 3, pp. 301–309. https://doi.org/10.1111/j.1398-9995.2006.01293.x
  3. D'Amato G., Cecchi L., Liccardi G. Thunderstorm-related asthma: not only grass pollen and spores. Journal of Allergy and Clinical Immunology, 2008, vol. 121, no. 2, pp. 537–539. https://doi.org/10.1016/j.jaci.2007.10.046
  4. De Weger L.A., Bergmann K.C., Rantio-Lehtimäki A., Dahl A., Buters J., Déchamp C., Belmonte J., Thibaudon M., Cecchi L., Besancenot J.-P., Galán C., Waisel Y. Impact of Pollen. Allergenic Pollen: A Review of the Production, Release, Distribution and Health Impacts. Springer, 2013, pp. 161–215. https://doi.org/10.1007/978-94-007-4881-1_6
  5. Jaeger S. The trouble with threshold values for allergy forecasts. Aerobiological Monographs. Towards a comprehensive vision. Ed. by B. Clot, P. Comtois, B. Escamilla-Garcia, 2006, pp. 233–245.
  6. Caillaud D.M., Martin S., Segala C., Besancenot J.-P., Clot B., Thibaudon M., Nonlinear short-term effects of airborne Poaceae levels on hay fever symptoms. Journal of Allergy and Clinical Immunology, 2012, vol. 130, no. 3, pp. 812–814. https://doi.org/10.1016/j.jaci.2012.04.034
  7. Committee for Medicinal Products for Human Use. European Medicines Agency Committee for Medicinal Products for Human Use (CHMP) guideline on the evaluation of anticancer medicinal products in man. London, UK, European Medicines Agency, 2006.
  8. Sikoparija B., Skjøth C.A., Celenk S. et al. Spatial and temporal variations in airborne Ambrosia pollen in Europe. Aerobiologia, 2017, vol. 33, no. 2, pp. 181–189. https://doi.org/10.1007/s10453-016-9463-1
  9. Novoselova L.V., Minaeva N. Pollen monitoring in Perm Krai (Russia)-experience of 6 years. Acta Agrobotanica, 2015, vol. 68, no. 4, pp. 343–348. https://doi.org/10.5586/aa.2015.042
  10. Pfaar O., Bastl K., Berger U., Buters J., Calderon M.A., Clot B., Darsow U., Demoly P., Durham S.R., Gala’n C., Gehrig R., Gerth van Wijk R., Jacobsen L., Klimek L., Sofiev M., Thibaudon M., Bergmann K.C. Definition von Pollenexpositionszeiten für klinische Studien zur Allergen-Immuntherapie bei polleninduzierter Rhinokonjunktivitis–ein EAACI-Positionspapier. Allergologie, 2018, vol. 41, no. 9, pp. 386–389. (in German). https://doi.org/10.5414/ALX02053
  11. Holt K.A., Bennett K.D. Principles and methods for automated palynology. New Phytologist, 2014, vol. 203, no. 3, pp. 735–742. https://doi.org/10.1111/nph.12848
  12. Flenley J.R. The problem of pollen recognition. Problems in Picture Interpretation. Ed. by M.B. Clowes, J.P. Penny. Canberra, CSIRO, 1968, pp. 141–145.
  13. Boucher A., Hidalgo P.J., Thonnat M., Belmonte J., Galan C., Bonton P., Tomczak R. Development of a semi-automatic system for pollen recognition. Aerobiologia, 2002, vol. 18, no. 3-4, pp. 195–201. https://doi.org/10.1023/A:1021322813565
  14. Chen C., Hendriks E.A., Duin R.P.W., Reiber J.H.C., Hiemstra P.S., de Weger L.A., Stoel B.C. Feasibility study on automated recognition of allergenic pollen: grass, birch and mugwort.Aerobiologia, 2006, vol. 22, no. 4, pp. 275–284. https://doi.org/10.1007/s10453-006-9040-0
  15. Ronneberger O., Schultz E., Burkhardt H. Automated pollen recognition using 3D volume images from fluorescence microscopy. Aerobiologia, 2002, vol. 18, no. 2, pp. 107–115. https://doi.org/10.1023/A:1020623724584
  16. Chica M. Authentication of bee pollen grains in bright‐field microscopy by combining one‐class classification techniques and image processing. Microscopy Research and Technique, 2012, vol. 75, no. 11, pp. 1475–1485. https://doi.org/10.1002/jemt.22091
  17. Chudyk C., Castaneda H., Leger R., Yahiaoui I., Boochs F. Development of an automatic pollen classification system using shape, texture and aperture features. CEUR Workshop Proceedings, 2015, vol. 1458, pp. 65–74.
  18. Khanzhina N., Putin E. Pollen recognition for allergy and asthma management using gist features. Communications in Computer and Information Science, 2016, vol. 674, pp. 515–525. https://doi.org/10.1007/978-3-319-49700-6_51
  19. Khanzhina N., Putin E., Filchenkov A., Zamyatina E. Pollen grain recognition using convolutional neural network. Proc. 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2018, pp. 409–414.
  20. Daood A., Ribeiro E., Bush M. Sequential recognition of pollen grain Z-stacks by combining CNN and RNN. Proc. 31st International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2018, pp. 8–13.
  21. Sevillano V., Holt K., Aznarte J.L. Precise automatic classification of 46 different pollen types with convolutional neural networks. PLoS ONE, 2020, vol. 15, no. 6, pp. e0229751. https://doi.org/10.1371/journal.pone.0229751
  22. Schiele J., Rabe F., Schmitt M., Glaser M., Häring F., Brunner J.O., Bauer B., Schuller B., Traidl-Hoffmann C., Damialis A. Automated classification of airborne pollen using neural networks. Proc. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 4474–4478. https://doi.org/10.1109/EMBC.2019.8856910
  23. Northcutt C.G., Athalye A., Mueller J. Pervasive label errors in test sets destabilize machine learning benchmarks. arXiv.org, 2021, arXiv:2103.14749.
  24. Kendall A., Gal Y. What uncertainties do we need in bayesian deep learning for computer vision? Proc. 31st Annual Conference on Neural Information Processing Systems (NIPS), 2017, pp. 5575–5585.
  25. Cipolla R., Gal Y., Kendall A. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. Proc. 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7482–7491. https://doi.org/10.1109/CVPR.2018.00781
  26. Bendale A., Boult T.E. Towards open set deep networks. Proc. 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1563–1572. https://doi.org/10.1109/CVPR.2016.173
  27. Harakeh A., Smart M., Waslander S.L. BayesOD: A bayesian approach for uncertainty estimation in deep object detectors. Proc. 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 87–93. https://doi.org/10.1109/ICRA40945.2020.9196544
  28. Wirges S., Reith-Braun M., Lauer M., Stiller C. Capturing object detection uncertainty in multi-layer grid maps. Proc. 30th IEEE Intelligent Vehicles Symposium, 2019, pp. 1520–1526. https://doi.org/10.1109/IVS.2019.8814073
  29. Miller D., Nicholson L., Dayoub F., Sünderhauf N. Dropout sampling for robust object detection in open-set conditions. Proc. 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 3243–3249. https://doi.org/10.1109/ICRA.2018.8460700
  30. Miller D., Dayoub F., Milford M., Sünderhauf N. Evaluating merging strategies for sampling-based uncertainty techniques in object detection. Proc. 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 2348–2354. https://doi.org/10.1109/ICRA.2019.8793821
  31. Miller D., Sünderhauf N., Milford M., Dayoub F. Uncertainty for identifying open-set errors in visual object detection. arXiv.org, 2021, arXiv:2104.01328.
  32. Postels J., Ferroni F., Coskun H., Navab N., Tombari F. Sampling-free epistemic uncertainty estimation using approximated variance propagation. Proc. 17th IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 2931–2940. https://doi.org/10.1109/ICCV.2019.00302
  33. Kraus F., Dietmayer K. Uncertainty estimation in one-stage object detection. Proc. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 53–60. https://doi.org/10.1109/ITSC.2019.8917494
  34. Le M.T., Diehl F., Brunner T., Knol A. Uncertainty estimation for deep neural object detectors in safety-critical applications. Proc. 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 3873–3878. https://doi.org/10.1109/ITSC.2018.8569637
  35. Lin T.-Y., Goyal P., Girshick R., He K., Dollar P. Focal loss for dense object detection. Proc. 16th IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2980–2988. https://doi.org/10.1109/ICCV.2017.324
  36. Huber P.J. Robust estimation of a location parameter. Breakthroughs in Statistics. New York, NY, Springer, 1992, pp. 492–518.
  37. Handbook of Mathematical Functions: With Formulas, Graphs, and Mathematical Tables. Ed. by M. Abramowitz, I.A. Stegun, R.H. Romer. U.S. Government Printing Office, 1988, 1046 p.
  38. Khanzhina N., Lapenok L., Filchenkov A. Towards robust object detection: Bayesian RetinaNet for homoscedastic aleatoric uncertainty modeling: preprint. Submitted to the 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021). Available at: http://genome.ifmo.ru/files/papers_files/UAI (accessed: 17.06.2021)
  39. He K., Zhang X., Ren S., Sun J. Deep residual learning for image recognition. Proc. 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90
  40. Chollet F. et al. Keras: The python deep learning library. Astrophysics Source Code Library, 2018, P. ascl: 1806.022.
  41. Kingma D.P., Ba J.L. Adam: A method for stochastic optimization. Proc. 3rd International Conference on Learning Representations (ICLR), 2015.
  42. Du X., Lin T.-Y., Jin P., Ghiasi G., Tan M., Cui Y., Le Q.V., Song X. SpineNet: Learning scale-permuted backbone for recognition and localization. Proc. of the IEEE/CVF Conference on Computer Visionand Pattern Recognition (CVPR), 2020, pp. 11589–11598. https://doi.org/10.1109/CVPR42600.2020.01161
  43. Zhang S., Chi C., Yao Y., Lei Z., Li S.Z. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proc. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 9756–9765. https://doi.org/10.1109/CVPR42600.2020.00978


Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Copyright 2001-2024 ©
Scientific and Technical Journal
of Information Technologies, Mechanics and Optics.
All rights reserved.

Яндекс.Метрика