Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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A review of Explainable AI Techniques in Medical Imaging

의료영상 분야를 위한 설명가능한 인공지능 기술 리뷰

  • Lee, DongEon (Department of Information Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University) ;
  • Park, ChunSu (Department of Information Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University) ;
  • Kang, Jeong-Woon (Department of Information Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University) ;
  • Kim, MinWoo (School of Biomedical Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University)
  • 이동언 (부산대학교 정보융합공학과) ;
  • 박춘수 (부산대학교 정보융합공학과) ;
  • 강정운 (부산대학교 정보융합공학과) ;
  • 김민우 (부산대학교 의생명융합공학과)
  • Received : 2022.06.17
  • Accepted : 2022.08.17
  • Published : 2022.08.31
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Abstract

Artificial intelligence (AI) has been studied in various fields of medical imaging. Currently, top-notch deep learning (DL) techniques have led to high diagnostic accuracy and fast computation. However, they are rarely used in real clinical practices because of a lack of reliability concerning their results. Most DL models can achieve high performance by extracting features from large volumes of data. However, increasing model complexity and nonlinearity turn such models into black boxes that are seldom accessible, interpretable, and transparent. As a result, scientific interest in the field of explainable artificial intelligence (XAI) is gradually emerging. This study aims to review diverse XAI approaches currently exploited in medical imaging. We identify the concepts of the methods, introduce studies applying them to imaging modalities such as computational tomography (CT), magnetic resonance imaging (MRI), and endoscopy, and lastly discuss limitations and challenges faced by XAI for future studies.

Keywords

Acknowledgement

본 연구는 2021학년도 부산대학교 4단계 BK21 대학원혁신지원사업에 의한 연구임. 이 논문은 2020년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임(No. 2020-0-01450, 인공지능융합연구센터지원(부산대학교)).

References

  1. J. Ker, L. Wang, J. Rao, and T. Lim. Deep Learning Applications in Medical Image Analysis. IEEE Access. 2018;6:9375-9389. https://doi.org/10.1109/access.2017.2788044
  2. Y. Chen, Y. Xie, Z. Zhou, F. Shi, A. G. Christodoulou, and D. Li. Brain MRI super resolution using 3D deep densely connected neural networks. in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). 2018; 739-742.
  3. K. Christensen-Jeffries et al. Super-resolution Ultrasound Imaging. Ultrasound in Medicine & Biology, 2020;46(4):865-891. https://doi.org/10.1016/j.ultrasmedbio.2019.11.013
  4. P. Teikari, R. P. Najjar, L. Schmetterer, and D. Milea. Embedded deep learning in ophthalmology: making ophthalmic imaging smarter. Ophthalmol Eye Dis. 2019;11.
  5. M. I. Razzak, S. Naz, and A. Zaib. Deep Learning for Medical Image Processing: Overview, Challenges and the Future. Springer International Publishing, 2018:323-350.
  6. A. Adadi and M. Berrada. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access. 2018;6: 52138-52160. https://doi.org/10.1109/access.2018.2870052
  7. T. Viering, Z. Wang, M. Loog, and E. Eisemann. How to Manipulate CNNs to Make Them Lie: the GradCAM Case. arXiv. 2022: 1907.10901.
  8. C. Longoni, A. Bonezzi, and C. K. Morewedge. Resistance to Medical Artificial Intelligence. Journal of Consumer Research. 2019;46(4):629-650. https://doi.org/10.1093/jcr/ucz013
  9. R. Yokoi, Y. Eguchi, T. Fujita, and K. Nakayachi. Artificial Intelligence Is Trusted Less than a Doctor in Medical Treatment Decisions: Influence of Perceived Care and Value Similarity. International Journal of Human-Computer Interaction. 2021;37(10):981-990. https://doi.org/10.1080/10447318.2020.1861763
  10. S. Reddy, S. Allan, S. Coghlan, and P. Cooper. A governance model for the application of AI in health care. Journal of the American Medical Informatics Association. 2020;27(3):491-497. https://doi.org/10.1093/jamia/ocz192
  11. C. Cath. Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2018;376(2133):20180080.
  12. D. Lee and S. N. Yoon. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. International Journal of Environmental Research and Public Health. 2021;18(1):1.
  13. M. Temme. Algorithms and Transparency in View of the New General Data Protection Regulation. Eur. Data Prot. L. Rev. 2017;3(4):473-485. https://doi.org/10.21552/edpl/2017/4/9
  14. The Precise4Q consortium, J. Amann, A. Blasimme, E. Vayena, D. Frey, and V. I. Madai. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak.. 2020;20(1):310.
  15. A. M. Antoniadi et al. Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. Applied Sciences. 2021;11(11):11.
  16. C. Park et al. Diagnostic performance for detecting bone marrow edema of the hip on dual-energy CT: Deep learning model vs. musculoskeletal physicians and radiologists. European Journal of Radiology. 2022;152:110337.
  17. M. T. Ribeiro, S. Singh, and C. Guestrin. 'Why Should I Trust You?': Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016;1135-1144.
  18. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision. 2004;59(2):167-181. https://doi.org/10.1023/B:VISI.0000022288.19776.77
  19. S. Lundberg and S.-I. Lee. A Unified Approach to Interpreting Model Predictions. Advances in neural information processing systems. 2017;30.
  20. H. A. Simon. Review of Theory of Games and Economic Behavior. American Journal of Sociology. 1945;50(6):558-560. https://doi.org/10.1086/219699
  21. O. Morgenstern and J. V. Neumann. Theory of Games and Economic Behavior, 3rd edition. Princeton: Princeton University Press; 1980.
  22. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Learning Deep Features for Discriminative Localization. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016; 2921-2929.
  23. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization. Proceedings of the IEEE international conference on computer vision. 2017; 618-626.
  24. A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian. Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Mar. 2018; 839-847.
  25. D. Omeiza, S. Speakman, C. Cintas, and K. Weldermariam. Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models. arXiv preprint arXiv:1908.01224, 2019.
  26. K. Li, Z. Wu, K.-C. Peng, J. Ernst, and Y. Fu. Tell Me Where to Look: Guided Attention Inference Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018;9215-9223.
  27. K. Li, Z. Wu, K.-C. Peng, J. Ernst, and Y. Fu. Guided Attention Inference Network. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020;42(12): 2996-3010. https://doi.org/10.1109/tpami.2019.2921543
  28. S. M. Muddamsetty, N. S. J. Mohammad, and T. B. Moeslund. SIDU: Similarity Difference And Uniqueness Method for Explainable AI. in 2020 IEEE International Conference on Image Processing (ICIP), Oct. 2020; 3269-3273.
  29. J. M. Kim, J. Choe, Z. Akata, and S. J. Oh. Keep CALM and Improve Visual Feature Attribution. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021; 8350-8360.
  30. K. Simonyan, A. Vedaldi, and A. Zisserman. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv preprint arXiv:1312.6034, 2013.
  31. J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim. Sanity checks for saliency maps. in Proceedings of the 32nd International Conference on Neural Information Processing Systems, Red Hook, NY, USA, Dec. 2018;9525-9536.
  32. D. Smilkov, N. Thorat, B. Kim, F. Viegas, and M. Wattenberg. SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825, 2017.
  33. S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Muller, and W. Samek. On Pixel-Wise Explanations for NonLinear Classifier Decisions by Layer-Wise Relevance Propagation. PLOS ONE. 2015;10(7);e0130140.
  34. B. Kim et al.. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). in Proceedings of the 35th International Conference on Machine Learning, Jul. 2018; 2668-2677.
  35. F. Doshi-Velez and B. Kim. Towards A Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608, 2017.
  36. C.-K. Yeh, B. Kim, S. Arik, C.-L. Li, P. Ravikumar, and T. Pfister. On Concept-Based Explanations in Deep Neural Networks. 2019.
  37. P. W. Koh et al. Concept Bottleneck Models. in International Conference on Machine Learning, Nov. 2020;5338-5348.
  38. A. Margeloiu, M. Ashman, U. Bhatt, Y. Chen, M. Jamnik, and A. Weller. Do Concept Bottleneck Models Learn as Intended?. arXiv preprint arXiv:2105.04289, 2021.
  39. R. Soni, N. Shah, C. T. Seng, and J. D. Moore. Adversarial TCAV -- Robust and Effective Interpretation of Intermediate Layers in Neural Networks. arXiv preprint arXiv:2002.03549, 2020.
  40. D. Bahdanau, K. Cho, and Y. Bengio. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv preprint arXiv:1409.0473, 2014.
  41. A. Vaswani et al. Attention Is All You Need. Advances in neural information processing systems. 2017;30.
  42. A. Dosovitskiy et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv preprint arXiv:2010.11929, 2020.
  43. J. Chen et al. TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv preprint arXiv:2102.04306, 2021.
  44. J. Chen, Y. He, E. C. Frey, Y. Li, and Y. Du. ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration. arXiv preprint arXiv:2104.06468, 2021.
  45. S. Abnar and W. Zuidema. Quantifying Attention Flow in Transformers. arXiv preprint arXiv:2005.00928, 2020.
  46. S. Knapic, A. Malhi, R. Saluja, and K. Framling. Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain. Machine Learning and Knowledge Extraction. 2021;3(3):3.
  47. I. Palatnik de Sousa, M. Maria Bernardes Rebuzzi Vellasco, and E. Costa da Silva. Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases. Sensors (Basel). 2019;19(13):2969.
  48. W. Hryniewska, P. Bombinski, P. Szatkowski, P. Tomaszewska, A. Przelaskowski, and P. Biecek. Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies. Pattern Recognition. 2021;118:108035.
  49. F. Arcadu, F. Benmansour, A. Maunz, J. Willis, Z. Haskova, and M. Prunotto. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. npj Digit. Med. 2019;2(1):1.
  50. A. Shrikumar, P. Greenside, and A. Kundaje. Learning Important Features Through Propagating Activation Differences. in Proceedings of the 34th International Conference on Machine Learning, Jul. 2017; 3145-3153.
  51. Z. Yang, L. Zhao, S. Wu, and C. Y.-C. Chen. Lung Lesion Localization of COVID-19 From Chest CT Image: A Novel Weakly Supervised Learning Method. IEEE Journal of Biomedical and Health Informatics. 2021;25(6):1864-1872. https://doi.org/10.1109/JBHI.2021.3067465
  52. S. Lee, J. Lee, J. Lee, C.-K. Park, and S. Yoon. Robust Tumor Localization with Pyramid Grad-CAM. arXiv preprint arXiv:1805.11393, 2018.
  53. S. Hicks et al. Dissecting Deep Neural Networks for Better Medical Image Classification and Classification Understanding. in 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Jun. 2018;363-368.
  54. K. Young, G. Booth, B. Simpson, R. Dutton, and S. Shrapnel. Deep Neural Network or Dermatologist?. in Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support, Cham, 2019;48-55.
  55. A. H. Ornek and M. Ceylan. Explainable Artificial Intelligence (XAI): Classification of Medical Thermal Images of Neonates Using Class Activation Maps. Traitement du Signal. 2021;38(5):1271-1279. https://doi.org/10.18280/ts.380502
  56. A. Ghorbani et al. Deep learning interpretation of echocardiograms. npj Digit. Med. 2020;3(1):1.
  57. O. Ben-Ahmed, F. Lecellier, M. Paccalin, and C. Fernandez-Maloigne. Multi-view visual saliency-based MRI classification for alzheimer's disease diagnosis. in 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Nov. 2017;1-6.
  58. F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers, and D. Pfeiffer. Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization. Sci Rep 2019;9(1):1.
  59. W. Gale, L. Oakden-Rayner, G. Carneiro, A. P. Bradley, and L. J. Palmer. Producing radiologist-quality reports for interpretable artificial intelligence. arXiv preprint arXiv:1806.00340, 2018.
  60. M. Bohle, F. Eitel, M. Weygandt, and K. Ritter. Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification. Front. Aging Neurosci. 2019;11:194.
  61. M. Graziani, V. Andrearczyk, and H. Muller. Regression Concept Vectors for Bidirectional Explanations in Histopathology. Understanding and Interpreting Machine Learning in Medical Image Computing Applications. Springer, Cham, 2018;124-132.
  62. A. Lucieri, M. N. Bajwa, S. A. Braun, M. I. Malik, A. Dengel, and S. Ahmed. On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors. 2020 International Joint Conference on Neural Networks (IJCNN). 2020;1-10.
  63. M. Reyes et al. On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities. Radiol Artif Intell. 2020;2(3):e190043.
  64. J. C. M. Than et al. Preliminary Study on Patch Sizes in Vision Transformers (ViT) for COVID-19 and Diseased Lungs Classification. in 2021 IEEE National Biomedical Engineering Conference (NBEC), Nov. 2021;146-150.
  65. G. Jacenkow, A. Q. O'Neil, and S. A. Tsaftaris. Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022;1-5.
  66. W. Zhang, W. Hao, Y. Qi, and Y. Wu. Application of Transformer in Medical Image Segmentation. Am J Biomed Sci & Res. 2021;14(4):3.
  67. C. Matsoukas, J. F. Haslum, M. Soderberg, and K. Smith. Is it Time to Replace CNNs with Transformers for Medical Images?. arXiv preprint arXiv:2108.09038, 2021.
  68. A. K. Mondal, A. Bhattacharjee, P. Singla, and A. P. Prathosh. xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography. IEEE Journal of Translational Engineering in Health and Medicine. 2022;10:1-10.
  69. F. Shamshad et al. Transformers in Medical Imaging: A Survey. arXiv preprint arXiv:2201.09873, 2022.
  70. B. H. M. van der Velden, M. A. A. Ragusi, M. H. A. Janse, C. E. Loo, and K. G. A. Gilhuijs. Interpretable deep learning regression for breast density estimation on MRI. Medical Imaging 2020: Computer-Aided Diagnosis. 2020;69.
  71. H. Choi, Y. K. Kim, E. J. Yoon, J.-Y. Lee, and D. S. Lee. Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease. Eur J Nucl Med Mol Imaging. 2020;47(2):403-412. https://doi.org/10.1007/s00259-019-04538-7
  72. Z. Lin et al. Multi-task learning for quality assessment of fetal head ultrasound images. Medical Image Analysis. 2019;58:101548.
  73. S. Pereira, R. Meier, V. Alves, M. Reyes, and C. A. Silva. Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment. in Understanding and Interpreting Machine Learning in Medical Image Computing Applications, Cham. 2018;106-114.
  74. G. M., A. V., M.-M. S., and M. H. Concept attribution: Explaining CNN decisions to physicians. Computers in Biology and Medicine. 2020;123:103865
  75. J. R. Clough, I. Oksuz, E. Puyol-Anton, B. Ruijsink, A. P. King, and J. A. Schnabel. Global and Local Interpretability for Cardiac MRI Classification. in Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, Cham. 2019;656-664.
  76. U. Klimiene et al. Multiview Concept Bottleneck Models Applied to Diagnosing Pediatric Appendicitis. 2022.
  77. I. Grigorescu, L. Cordero-Grande, A. David Edwards, J. V. Hajnal, M. Modat, and M. Deprez. Investigating Image Registration Impact on Preterm Birth Classification: An Interpretable Deep Learning Approach. in Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis, Cham, 2019;104-112.
  78. S. Dang and S. Chaudhury. Novel relative relevance score for estimating brain connectivity from fMRI data using an explainable neural network approach. Journal of Neuroscience Methods. 2019;326:108371.
  79. M. Hagele et al. Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. Sci Rep. 2020;10(1):1.
  80. J. Wang et al. An Attention-based Semi-supervised Neural Network for Thyroid Nodules Segmentation. in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Nov. 2019; 871-876.
  81. A. Holzinger and H. Muller. Toward Human-AI Interfaces to Support Explainability and Causability in Medical AI. Computer. 2021;54(10):78-86. https://doi.org/10.1109/MC.2021.3092610
  82. K. Pearson. LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. 1901;2(11): 559-572. https://doi.org/10.1080/14786440109462720
  83. L. van der Maaten and G. Hinton. Visualizing Data using tSNE. Journal of Machine Learning Research. 2008;9(86): 2579-2605.
  84. M. Li et al. FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark. Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). 2021.
  85. D. Garreau and D. Mardaoui. What does LIME really see in images?. in Proceedings of the 38th International Conference on Machine Learning, Jul. 2021;3620-3629.
  86. L. O. Teixeira et al. Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images. Sensors. 2021;21(21):21.
  87. B. Davis, M. Glenski, W. Sealy, and D. Arendt. Measure Utility, Gain Trust: Practical Advice for XAI Researchers. in 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX), Oct. 2020;1-8.
  88. J. Heo, S. Joo, and T. Moon. Fooling Neural Network Interpretations via Adversarial Model Manipulation. in Advances in Neural Information Processing Systems, 2019;32.
  89. E. Amparore, A. Perotti, and P. Bajardi. To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods. PeerJ Comput. Sci. 2021;7:e479.
  90. W. Jin, X. Li, and G. Hamarneh. One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal Medical Images. arXiv preprint arXiv:2107.05047, 2021.
  91. T. Evans et al. The explainability paradox: Challenges for xAI in digital pathology. Future Generation Computer Systems. 2022;133:281-296. https://doi.org/10.1016/j.future.2022.03.009