Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 Sep 2024
28 Sep 2024
Historique:
received:
29
04
2024
accepted:
20
09
2024
medline:
29
9
2024
pubmed:
29
9
2024
entrez:
28
9
2024
Statut:
epublish
Résumé
Recent developments have highlighted the critical role that computer-aided diagnosis (CAD) systems play in analyzing whole-slide digital histopathology images for detecting gastric cancer (GC). We present a novel framework for gastric histology classification and segmentation (GHCS) that offers modest yet meaningful improvements over existing CAD models for GC classification and segmentation. Our methodology achieves marginal improvements over conventional deep learning (DL) and machine learning (ML) models by adaptively focusing on pertinent characteristics of images. This contributes significantly to our study, highlighting that the proposed model, which performs well on normalized images, is robust in certain respects, particularly in handling variability and generalizing to different datasets. We anticipate that this robustness will lead to better results across various datasets. An expectation-maximizing Naïve Bayes classifier that uses an updated Gaussian Mixture Model is at the heart of the suggested GHCS framework. The effectiveness of our classifier is demonstrated by experimental validation on two publicly available datasets, which produced exceptional classification accuracies of 98.87% and 97.28% on validation sets and 98.47% and 97.31% on test sets. Our framework shows a slight but consistent improvement over previously existing techniques in gastric histopathology image classification tasks, as demonstrated by comparative analysis. This may be attributed to its ability to capture critical features of gastric histopathology images better. Furthermore, using an improved Fuzzy c-means method, our study produces good results in GC histopathology picture segmentation, outperforming state-of-the-art segmentation models with a Dice coefficient of 65.21% and a Jaccard index of 60.24%. The model's interpretability is complemented by Grad-CAM visualizations, which help understand the decision-making process and increase the model's trustworthiness for end-users, especially clinicians.
Identifiants
pubmed: 39342030
doi: 10.1038/s41598-024-73823-9
pii: 10.1038/s41598-024-73823-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
22533Subventions
Organisme : Khalifa University, United Arab Emirates
ID : 8474000409
Informations de copyright
© 2024. The Author(s).
Références
Wild, C. P., Stewart, B. W. & Wild, C. World cancer report 2014 (World Health Organization Geneva, Switzerland, 2014).
Garcia, E. et al. Automatic lymphocyte detection on gastric cancer ihc images using deep learning. In 2017 IEEE 30th international symposium on computer-based medical systems (CBMS), 200–204 (IEEE, 2017).
Wang, F.-H. et al. The chinese society of clinical oncology (csco): clinical guidelines for the diagnosis and treatment of gastric cancer. Cancer communications 39, 1–31 (2019).
pubmed: 30606259
pmcid: 6319003
Hohenberger, W., Weber, K., Matzel, K. & Papadopoulos, T. Standardized surgery for gastric cancer-german version. Oncology Research and Treatment 43, 689–696 (2020).
Lozano, R. Comparison of computer-assisted and manual screening of cervical cytology. Gynecologic oncology 104, 134–138 (2007).
pubmed: 16959306
doi: 10.1016/j.ygyno.2006.07.025
Sies, K. et al. Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions. European Journal of Cancer 135, 39–46 (2020).
pubmed: 32534243
doi: 10.1016/j.ejca.2020.04.043
Bi, D., Zhu, D., Sheykhahmad, F. R. & Qiao, M. Computer-aided skin cancer diagnosis based on a new meta-heuristic algorithm combined with support vector method. Biomedical Signal Processing and Control 68, 102631 (2021).
doi: 10.1016/j.bspc.2021.102631
Malibari, A. A. et al. Optimal deep neural network-driven computer aided diagnosis model for skin cancer. Computers and Electrical Engineering 103, 108318 (2022).
doi: 10.1016/j.compeleceng.2022.108318
Abràmoff, M. D. et al. Automated and computer-assisted detection, classification, and diagnosis of diabetic retinopathy. Telemedicine and e-Health 26, 544–550 (2020).
pubmed: 32209008
pmcid: 7187982
doi: 10.1089/tmj.2020.0008
Haider, A. et al. Artificial intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images. Expert Systems with Applications 207, 117968 (2022).
doi: 10.1016/j.eswa.2022.117968
Zubair, M. et al. A comprehensive computer-aided system for an early-stage diagnosis and classification of diabetic macular edema. Journal of King Saud University-Computer and Information Sciences 35, 101719 (2023).
doi: 10.1016/j.jksuci.2023.101719
Zubair, M. et al. Automated grading of diabetic macular edema using color retinal photographs. In 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), 1–6 (IEEE, 2022).
Zubair, M., Ali, H. & Javed, M. Y. Automated segmentation of hard exudates using dynamic thresholding to detect diabetic retinopathy in retinal photographs. J. Multim. Process. Technol. 7, 109–116 (2016).
Zubair, M., Yamin, A. & Khan, S. A. Automated detection of optic disc for the analysis of retina using color fundus image. In 2013 IEEE International Conference on Imaging Systems and Techniques (IST), 239–242 (IEEE, 2013).
Zubair, M., Khan, S. A. & Yasin, U. U. Classification of diabetic macular edema and its stages using color fundus image. Journal of Electronic Science and Technology 12, 187–190 (2014).
Mahmood, T., Arsalan, M., Owais, M., Lee, M. B. & Park, K. R. Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster r-cnn and deep cnns. Journal of clinical medicine 9, 749 (2020).
pubmed: 32164298
pmcid: 7141212
doi: 10.3390/jcm9030749
Aljuaid, H., Alturki, N., Alsubaie, N., Cavallaro, L. & Liotta, A. Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning. Computer Methods and Programs in Biomedicine 223, 106951 (2022).
pubmed: 35767911
doi: 10.1016/j.cmpb.2022.106951
Owais, M. et al. Multilevel deep-aggregated boosted network to recognize covid-19 infection from large-scale heterogeneous radiographic data. IEEE Journal of Biomedical and Health Informatics 25, 1881–1891 (2021).
pubmed: 33835928
doi: 10.1109/JBHI.2021.3072076
Khan, S. H. et al. Covid-19 detection and analysis from lung ct images using novel channel boosted cnns. Expert Systems with Applications 229, 120477 (2023).
pubmed: 37220492
pmcid: 10186852
doi: 10.1016/j.eswa.2023.120477
Zubair, M., Umair, M. & Owais, M. Automated brain tumor detection using soft computing-based segmentation technique. In 2023 3rd International Conference on Computing and Information Technology (ICCIT), 211–215 (IEEE, 2023).
Woźniak, M., Siłka, J. & Wieczorek, M. Deep neural network correlation learning mechanism for ct brain tumor detection. Neural Computing and Applications 35, 14611–14626 (2023).
doi: 10.1007/s00521-021-05841-x
Chen, B., Zhang, L., Chen, H., Liang, K. & Chen, X. A novel extended kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors. Computer Methods and Programs in Biomedicine 200, 105797 (2021).
pubmed: 33317871
doi: 10.1016/j.cmpb.2020.105797
Doi, K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics 31, 198–211 (2007).
pubmed: 17349778
pmcid: 1955762
doi: 10.1016/j.compmedimag.2007.02.002
An, P. et al. A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy. Gastric Cancer 23, 884–892 (2020).
pubmed: 32356118
doi: 10.1007/s10120-020-01071-7
Ling, T. et al. A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy. Endoscopy 53, 469–477 (2021).
pubmed: 32725617
doi: 10.1055/a-1229-0920
Zhang, K. et al. Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images. Scientific Reports 14, 7847 (2024).
pubmed: 38570595
pmcid: 10991264
doi: 10.1038/s41598-024-58361-8
Takemoto, S. et al. Computer-aided demarcation of early gastric cancer: a pilot comparative study with endoscopists. Journal of Gastroenterology 58, 741–750 (2023).
pubmed: 37256409
doi: 10.1007/s00535-023-02001-x
Yan, T. et al. Semantic segmentation of gastric polyps in endoscopic images based on convolutional neural networks and an integrated evaluation approach. Bioengineering 10, 806 (2023).
pubmed: 37508833
pmcid: 10376250
doi: 10.3390/bioengineering10070806
Wang, X. et al. Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning. Nature communications 12, 1637 (2021).
pubmed: 33712598
pmcid: 7954798
doi: 10.1038/s41467-021-21674-7
Sun, M. et al. Accurate gastric cancer segmentation in digital pathology images using deformable convolution and multi-scale embedding networks. IEEE access 7, 75530–75541 (2019).
doi: 10.1109/ACCESS.2019.2918800
Liang, Q. et al. Weakly supervised biomedical image segmentation by reiterative learning. IEEE Journal of biomedical and health informatics 23, 1205–1214 (2018).
pubmed: 29994489
doi: 10.1109/JBHI.2018.2850040
Han, C. et al. Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels. Medical Image Analysis 80, 102487 (2022).
pubmed: 35671591
doi: 10.1016/j.media.2022.102487
Sun, C. et al. Gastric histopathology image segmentation using a hierarchical conditional random field. Biocybernetics and Biomedical Engineering 40, 1535–1555 (2020).
doi: 10.1016/j.bbe.2020.09.008
Rai, H. M. Cancer detection and segmentation using machine learning and deep learning techniques: A review. Multimedia Tools and Applications 83, 27001–27035 (2024).
doi: 10.1007/s11042-023-16520-5
Ali, S. et al. Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge. Scientific Reports 14, 2032 (2024).
pubmed: 38263232
pmcid: 10805888
doi: 10.1038/s41598-024-52063-x
Atmakuru, A. et al. Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques. Expert Systems with Applications 124665 (2024).
Oyelade, O. N., Ezugwu, A. E., Venter, H. S., Mirjalili, S. & Gandomi, A. H. Abnormality classification and localization using dual-branch whole-region-based cnn model with histopathological images. Computers in Biology and Medicine 149, 105943 (2022).
pubmed: 35986967
doi: 10.1016/j.compbiomed.2022.105943
Liu, X., Jiao, L., Li, L., Tang, X. & Guo, Y. Deep multi-level fusion network for multi-source image pixel-wise classification. Knowledge-Based Systems 221, 106921 (2021).
doi: 10.1016/j.knosys.2021.106921
Ahmed, H., Le, C. P. & La, H. M. Pixel-level classification for bridge deck rebar detection and localization using multi-stage deep encoder-decoder network. Developments in the Built Environment 14, 100132 (2023).
doi: 10.1016/j.dibe.2023.100132
Hu, W. et al. Gashissdb: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer. Computers in biology and medicine 142, 105207 (2022).
pubmed: 35016101
doi: 10.1016/j.compbiomed.2021.105207
Sun, C., Li, C. & Li, Y. Data for hcrf. Mendeley Data, v2, http://dx. doi. org/10.17632/thgf23xgy7 2 (2020).
Reinhard, E., Adhikhmin, M., Gooch, B. & Shirley, P. Color transfer between images. IEEE Computer graphics and applications 21, 34–41 (2001).
doi: 10.1109/38.946629
Hu, W. et al. A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer. Frontiers in Medicine 9, 1072109 (2022).
pubmed: 36569152
pmcid: 9767945
doi: 10.3389/fmed.2022.1072109
Chollet, F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1251–1258 (2017).
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2818–2826 (2016).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012).
Howard, A. et al. Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision, 1314–1324 (2019).
Szegedy, C., Ioffe, S., Vanhoucke, V. & Alemi, A. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, vol. 31 (2017).
Tan, M. & Le, Q. Efficientnetv2: Smaller models and faster training. In International conference on machine learning, 10096–10106 (PMLR, 2021).
Breiman, L. Random forests. Machine learning 45, 5–32 (2001).
doi: 10.1023/A:1010933404324
Guo, G., Wang, H., Bell, D., Bi, Y. & Greer, K. Knn model-based approach in classification. In On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, November 3-7, 2003. Proceedings, 986–996 (Springer, 2003).
Cortes, C. & Vapnik, V. Support-vector networks. Machine learning 20, 273–297 (1995).
Yang, F.-J. An implementation of naive bayes classifier. In 2018 International conference on computational science and computational intelligence (CSCI), 301–306 (IEEE, 2018).
Dosovitskiy, A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).
Badrinarayanan, V., Kendall, A. & Cipolla, R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39, 2481–2495 (2017).
pubmed: 28060704
doi: 10.1109/TPAMI.2016.2644615
Huang, Y.-L. & Chen, D.-R. Watershed segmentation for breast tumor in 2-d sonography. Ultrasound in medicine & biology 30, 625–632 (2004).
doi: 10.1016/j.ultrasmedbio.2003.12.001
Siddique, N., Paheding, S., Elkin, C. P. & Devabhaktuni, V. U-net and its variants for medical image segmentation: A review of theory and applications. Ieee Access 9, 82031–82057 (2021).
doi: 10.1109/ACCESS.2021.3086020
You, M., Luo, C., Zhou, H. & Zhu, S. Dynamic dense crf inference for video segmentation and semantic slam. Pattern Recognition 133, 109023 (2023).
doi: 10.1016/j.patcog.2022.109023
Xu, X., Zhou, F. & Liu, B. Automatic bladder segmentation from ct images using deep cnn and 3d fully connected crf-rnn. International journal of computer assisted radiology and surgery 13, 967–975 (2018).
pubmed: 29556905
doi: 10.1007/s11548-018-1733-7