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AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans (2407.02418v2)

Published 2 Jul 2024 in eess.IV and cs.CV

Abstract: This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. At the same time, the importance of each slice in decision-making is learned, allowing the generation of a voxel-level attention map to produce an explainable MRI. To test our method and ensure the reproducibility of our results, we chose a standardized collection of MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). On this dataset, our method significantly outperforms state-of-the-art methods in (i) distinguishing AD from cognitive normal (CN) with an accuracy of 0.856 and Matthew's correlation coefficient (MCC) of 0.712, representing improvements of 2.4% and 5.3% respectively over the second-best, and (ii) in the prognostic task of discerning stable from progressive mild cognitive impairment (MCI) with an accuracy of 0.725 and MCC of 0.443, showing improvements of 10.2% and 20.5% respectively over the second-best. We achieved this prognostic result by adopting a double transfer learning strategy, which enhanced sensitivity to morphological changes and facilitated early-stage AD detection. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions: the hippocampus, the amygdala, the parahippocampal, and the inferior lateral ventricles. All these areas are clinically associated with AD development. Furthermore, our approach consistently found the same AD-related areas across different cross-validation folds, proving its robustness and precision in highlighting areas that align closely with known pathological markers of the disease.

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References (76)
  1. Preclinical stage alzheimer’s disease detection using magnetic resonance image scans, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 15088–15097.
  2. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis 12, 26–41.
  3. The insight toolkit image registration framework. Frontiers in neuroinformatics 8, 44.
  4. Automated classification of alzheimer’s disease and mild cognitive impairment using a single mri and deep neural networks. NeuroImage: Clinical 21, 101645.
  5. Forecasting the global burden of alzheimer’s disease. Alzheimer’s & dementia 3, 186–191.
  6. Convolution neural networks and self-attention learners for alzheimer dementia diagnosis from brain mri. Sensors 23, 1694.
  7. Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks, in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE. URL: http://dx.doi.org/10.1109/WACV.2018.00097, doi:10.1109/wacv.2018.00097.
  8. The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics 21, 1–13.
  9. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 .
  10. Mri-based automated computer classification of probable ad versus normal controls. IEEE transactions on medical imaging 27, 509–520.
  11. Deep sequence modelling for alzheimer’s disease detection using mri. Computers in Biology and Medicine 134, 104537.
  12. Neuroimaging markers for the prediction and early diagnosis of alzheimer’s disease dementia. Trends in neurosciences 34, 430–442.
  13. Multivariate data analysis and machine learning in alzheimer’s disease with a focus on structural magnetic resonance imaging. Journal of Alzheimer’s disease 41, 685–708.
  14. A deep learning mri approach outperforms other biomarkers of prodromal alzheimer’s disease. Alzheimer’s Research & Therapy 14, 45.
  15. Shape differences of the brain ventricles in alzheimer’s disease. Neuroimage 32, 1060–1069.
  16. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54, 313–327.
  17. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47, S102.
  18. The clinical use of structural mri in alzheimer disease. Nature Reviews Neurology 6, 67–77.
  19. Patch-based classification for alzheimer disease using smri, in: 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), IEEE. pp. 1–5.
  20. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data 3, 1–9.
  21. Principles of classification analyses in mild cognitive impairment (mci) and alzheimer disease. Journal of Alzheimer’s Disease 26, 389–394.
  22. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in: Proceedings of the IEEE international conference on computer vision, pp. 1026–1034.
  23. Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society. pp. 770–778. doi:10.1109/CVPR.2016.90.
  24. Towards alzheimer’s disease classification through transfer learning, in: 2017 IEEE International conference on bioinformatics and biomedicine (BIBM), IEEE. pp. 1166–1169.
  25. Conv-swinformer: Integration of cnn and shift window attention for alzheimer’s disease classification. Computers in Biology and Medicine 164, 107304.
  26. Vgg-tswinformer: Transformer-based deep learning model for early alzheimer’s disease prediction. Computer Methods and Programs in Biomedicine 229, 107291.
  27. Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708.
  28. Mri as a biomarker of disease progression in a therapeutic trial of milameline for ad. Neurology 60, 253–260.
  29. Fsl. Neuroimage 62, 782–790.
  30. Learn to pay attention. arXiv preprint arXiv:1804.02391 .
  31. Attention-based 3d convolutional network for alzheimer’s disease diagnosis and biomarkers exploration, in: 2019 IEEE 16Th international symposium on biomedical imaging (ISBI 2019), IEEE. pp. 1047–1051.
  32. Multi-model and multi-slice ensemble learning architecture based on 2d convolutional neural networks for alzheimer’s disease diagnosis. Computers in Biology and Medicine 136, 104678.
  33. A systematic literature review on transfer learning for 3d-cnns, in: 2021 international joint conference on neural networks (IJCNN), IEEE. pp. 1–10.
  34. Automatic classification of mr scans in alzheimer’s disease. Brain 131, 681–689.
  35. Addformer: Alzheimer’s disease detection from structural mri using fusion transformer, in: 2022 IEEE 19th International Symposium On Biomedical Imaging (ISBI), IEEE. pp. 1–5.
  36. Clinical, imaging, and pathological heterogeneity of the alzheimer’s disease syndrome. Alzheimer’s research & therapy 5, 1–14.
  37. Patch-based deep multi-modal learning framework for alzheimer’s disease diagnosis using multi-view neuroimaging. Biomedical Signal Processing and Control 80, 104400.
  38. Cognitive profiles in alzheimer’s disease and in mild cognitive impairment of different etiologies. Dementia and geriatric cognitive disorders 21, 309–315.
  39. A transfer learning approach for early diagnosis of alzheimer’s disease on mri images. Neuroscience 460, 43–52.
  40. Persistent neuropathological effects 14 years following amyloid-β𝛽\betaitalic_β immunization in alzheimer’s disease. Brain 142, 2113–2126.
  41. Early detection of alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Frontiers in neuroscience 14, 259.
  42. Deep joint learning of pathological region localization and alzheimer’s disease diagnosis. Scientific reports 13, 11664.
  43. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32.
  44. Amygdala atrophy is prominent in early alzheimer’s disease and relates to symptom severity. Psychiatry Research: Neuroimaging 194, 7–13.
  45. Development and validation of an interpretable deep learning framework for alzheimer’s disease classification. Brain 143, 1920–1933.
  46. Global filter networks for image classification. Advances in neural information processing systems 34, 980–993.
  47. Hippocampus and its involvement in alzheimer’s disease: a review. 3 Biotech 12, 55.
  48. A review on neuroimaging-based classification studies and associated feature extraction methods for alzheimer’s disease and its prodromal stages. NeuroImage 155, 530–548.
  49. Clinica: An open-source software platform for reproducible clinical neuroscience studies. Frontiers in Neuroinformatics 15, 689675.
  50. Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252.
  51. Reproducible evaluation of classification methods in alzheimer’s disease: Framework and application to mri and pet data. NeuroImage 183, 504–521.
  52. Attention gated networks: Learning to leverage salient regions in medical images. Medical image analysis 53, 197–207.
  53. Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE international conference on computer vision, pp. 618–626.
  54. Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556. URL: http://arxiv.org/abs/1409.1556, arXiv:1409.1556.
  55. Explainable deep learning models in medical image analysis. Journal of imaging 6, 52.
  56. Fast robust automated brain extraction. Human brain mapping 17, 143–155.
  57. Efficientnetv2: Smaller models and faster training. arXiv preprint arXiv:2104.00298 .
  58. Classification of alzheimer’s disease using ensemble of deep neural networks trained through transfer learning. IEEE Journal of Biomedical and Health Informatics 26, 1453–1463.
  59. N4itk: Improved n3 bias correction. IEEE Transactions on Medical Imaging 29, 1310–1320. doi:10.1109/TMI.2010.2046908.
  60. The parahippocampal gyrus in alzheimer’s disease: clinical and preclinical neuroanatomical correlates. Annals of the New York Academy of Sciences 911, 254–274.
  61. Explainable artificial intelligence (xai) in deep learning-based medical image analysis. Medical Image Analysis 79, 102470.
  62. Mri and csf biomarkers in normal, mci, and ad subjects: diagnostic discrimination and cognitive correlations. Neurology 73, 287–293.
  63. Multimodal deep learning models for early detection of alzheimer’s disease stage. Scientific reports 11, 3254.
  64. Explainable artificial intelligence in alzheimer’s disease classification: A systematic review. Cognitive Computation 16, 1–44.
  65. Joint learning framework of cross-modal synthesis and diagnosis for alzheimer’s disease by mining underlying shared modality information. Medical Image Analysis 91, 103032.
  66. Classification of alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. Journal of medical systems 42, 1–11.
  67. Convolutional neural networks for classification of alzheimer’s disease: Overview and reproducible evaluation. Medical image analysis 63, 101694.
  68. Defeating alzheimer’s disease and other dementias: a priority for european science and society. The Lancet Neurology 15, 455–532.
  69. Economic burden of alzheimer disease and managed care considerations. The American journal of managed care 26, S177–S183.
  70. An attention-based 3d cnn with multi-scale integration block for alzheimer’s disease classification. IEEE Journal of Biomedical and Health Informatics 26, 5665–5673.
  71. Standardization of analysis sets for reporting results from adni mri data. Alzheimer’s & Dementia 9, 332–337.
  72. Pybids: Python tools for bids datasets. Journal of Open Source Software 4, 1294. URL: https://doi.org/10.21105/joss.01294, doi:10.21105/joss.01294.
  73. Pybids: Python tools for bids datasets. URL: https://doi.org/10.5281/zenodo.8253830, doi:10.5281/zenodo.8253830.
  74. How transferable are features in deep neural networks? Advances in neural information processing systems 27.
  75. Diagnosis of alzheimer’s disease based on regional attention with smri gray matter slices. Journal of neuroscience methods 365, 109376.
  76. A survey of deep learning for alzheimer’s disease. Machine Learning and Knowledge Extraction 5, 611–668.
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