Acceso abierto

Explainable patch-level histopathology tissue type detection with bag-of-local-features models and data augmentation


Cite

K. AbdulJabbar, S. E. A. Raza, R. Rosenthal, M. Jamal-Hanjani, S. Veeriah, A. Akarca, T. Lund, D. A. Moore, R. Salgado, M. Al Bakir, L. Zapata, Geospatial immune variability illuminates differential evolution of lung adenocarcinoma, Nature Medicine 26(7), 2020, pp. 1054–1062. ⇒61 Search in Google Scholar

M. Amgad, H. Elfandy, H. Hussein, L. A. Atteya, M. A. Elsebaie, L. S. Abo Elnasr, R. A. Sakr, H. S. Salem, A. F. Ismail, A. M. Saad, J. Ahmed, Structured crowdsourcing enables convolutional segmentation of histology images, Bioinformatics 35(18), 2019, pp. 3461–3467. ⇒61, 63, 64 Search in Google Scholar

L. Arras, A. Osman, W. Samek, CLEVR-XAI: a benchmark dataset for the ground truth evaluation of neural network explanations, Information Fusion 81, 2022, pp. 14–40. ⇒76 Search in Google Scholar

M. S. Ayhan, L. B. Kümmerle, L. Kühlewein, W. Inho en, G. Aliyeva, F. Ziemssen, P. Berens, Clinical validation of saliency maps for understanding deep neural networks in ophthalmology, Medical Image Analysis 77, 2022, p. 102364. ⇒68 Search in Google Scholar

W. Brendel, M. Bethge, Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet, arXiv preprint arXiv:1904.00760, 2019. ⇒62, 63, 66, 67, 68, 69, 72 Search in Google Scholar

C. Chen, O. Li, D. Tao, A. Barnett, C. Rudin, J. K. Su, This looks like that: deep learning for interpretable image recognition, Advances in neural information processing systems 32, 2019. ⇒72 Search in Google Scholar

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, L. Fei-Fei, ImageNet: A large-scale hierarchical image database, IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255). ⇒63 Search in Google Scholar

I. B. Dimenstein, Grossing biopsies: an introduction to general principles and techniques, Annals of Diagnostic Pathology 13(2), 2009, pp. 106–113. ⇒61 Search in Google Scholar

K. Doi, Current status and future potential of computer-aided diagnosis in medical imaging, The British Journal of Radiology 78, 2005, pp. 3–19. ⇒61 Search in Google Scholar

J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, T. Chen, Recent advances in convolutional neural networks, Pattern Recognition 77, 2018, pp. 354–377. ⇒67 Search in Google Scholar

C. Han, J. Lin, J. Mai, Y. Wang, Q. Zhang, B. Zhao, X. Chen, X. Pan, Z. Shi, Z. Xu, S. Yao, Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels, Medical Image Analysis 80, 2022, p. 102487. ⇒61, 62, 63, 71, 76 Search in Google Scholar

D. Hanahan, R. A. Weinberg, Hallmarks of cancer: the next generation, Cell 144(5), 2011, pp. 646–674. ⇒61 Search in Google Scholar

K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. ⇒67 Search in Google Scholar

L. Hou, D. Samaras, T. M. Kurc, Y. Gao, J. E. Davis, J. H. Saltz, Patch-based convolutional neural network for whole slide tissue image classification, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2424–2433. ⇒72 Search in Google Scholar

N. Kanwal, F. Pérez-Bueno, A. Schmidt, K. Engan, R. Molina, The devil is in the details: whole slide image acquisition and processing for artifacts detection, color variation, and data augmentation: a review, IEEE Access 10, 2022, pp. 58821–58844. ⇒61, 74 Search in Google Scholar

S. Morales, K. Engan, V. Naranjo, Artificial intelligence in computational pathology -– challenges and future directions, Digital Signal Processing 119, 2021, p. 103196. ⇒61 Search in Google Scholar

C. Park, H. I. Suk, Deep joint learning of pathological region localization and Alzheimer’s disease diagnosis, arXiv preprint arXiv:2108.04555, 2021. ⇒63, 68 Search in Google Scholar

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, ImageNet large scale visual recognition challenge, International Journal of Computer Vision 115, 2015, pp. 211–252. ⇒63 Search in Google Scholar

J. Su, D. V. Vargas, K. Sakurai, One pixel attack for fooling deep neural networks, IEEE Transactions on Evolutionary Computation 23(5), 2019, pp. 828–841. ⇒61 Search in Google Scholar

A. Taleb, W. Loetzsch, N. Danz, J. Severin, T. Gaertner, B. Bergner, C. Lippert, 3D self-supervised methods for medical imaging, Advances in Neural Information Processing Systems 33, 2020, pp. 18158–18172. ⇒61, 63 Search in Google Scholar

E. Tjoa, C. Guan, A survey on explainable artificial intelligence (XAI): Toward medical XAI, IEEE Transactions on Neural Networks and Learning Systems 32(11), 2020, pp. 4793–4813. ⇒61 Search in Google Scholar

B. H. Van der Velden, H. J. Kuijf, K. G. Gilhuijs, M. A. Viergever, Explainable artificial intelligence (XAI) in deep learning-based medical image analysis, Medical Image Analysis, 2022, p. 102470. ⇒61 Search in Google Scholar

J. Wang, H. Liu, X. Wang, L. Jing, Interpretable image recognition by constructing transparent embedding space, In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 895-904. ⇒72 Search in Google Scholar

M. Wang, D. Zhang, D. Shen, M. Liu, Multi-task exclusive relationship learning for Alzheimer’s disease progression prediction with longitudinal data, Medical Image Analysis 53, 2019, pp. 111–122 ⇒61 Search in Google Scholar

C. Wang, X. Zhu, J. C. Hong, D. Zheng, Artificial intelligence in radiotherapy treatment planning: present and future, Technology in Cancer Research & Treatment 18, 2019, p. 1533033819873922. ⇒61 Search in Google Scholar

eISSN:
2066-7760
Idioma:
Inglés
Calendario de la edición:
2 veces al año
Temas de la revista:
Computer Sciences, other