{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T12:01:23Z","timestamp":1747224083097,"version":"3.40.5"},"reference-count":62,"publisher":"IGI Global","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,4]]},"abstract":"<jats:p>This article presents a new fine-tuning framework for histopathological images analysis. Despite the most common solutions where the ImageNet models are reused for image classification, this research sets out to perform an intra-domain fine tuning between the trained models on the histopathological images. The purpose is to take advantage of the hypothesis on the efficiency of transfer learning between non-distant datasets and to examine for the first time these suggestions on the histopathological images. The Inception-v3 convolutional neural network architecture, six histopathological source datasets, and four target sets as base modules were used in this article. The obtained results reveal the importance of the pre-trained histopathological models compared to the ImageNet model. In particular, the ICIAR 2018-A presented a high-quality source model for the various target tasks due to its capacity in generalization. Finally, the comparative study with the other literature results shows that the proposed method achieved the best results on both CRC (95.28%) and KIMIA-PATH (98.18%) datasets.<\/jats:p>","DOI":"10.4018\/ijssmet.2020040102","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T19:40:02Z","timestamp":1581104402000},"page":"16-40","source":"Crossref","is-referenced-by-count":2,"title":["A New Intra Fine-Tuning Method Between Histopathological Datasets in Deep Learning"],"prefix":"10.4018","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8683-3163","authenticated-orcid":true,"given":"Nassima","family":"Dif","sequence":"first","affiliation":[{"name":"EEDIS Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3391-6280","authenticated-orcid":true,"given":"Zakaria","family":"Elberrichi","sequence":"additional","affiliation":[{"name":"EEDIS Laboraory, Djillali Liabes University, Sidi Bel Abbes, Algeria"}]}],"member":"2432","reference":[{"key":"IJSSMET.2020040102-0","first-page":"1","article-title":"Comparing LBP, HOG and deep features for classification of histopathology images.","author":"T. J.Alhindi","year":"2018","journal-title":"2018 International Joint Conference on Neural Networks (IJCNN)"},{"key":"IJSSMET.2020040102-1","first-page":"1","article-title":"Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network.","author":"M. Z.Alom","year":"2019","journal-title":"Journal of Digital Imaging"},{"issue":"6","key":"IJSSMET.2020040102-2","doi-asserted-by":"crossref","first-page":"e0177544","DOI":"10.1371\/journal.pone.0177544","article-title":"Classification of breast cancer histology images using convolutional neural networks.","volume":"12","author":"T.Ara\u00fajo","year":"2017","journal-title":"PLoS One"},{"key":"IJSSMET.2020040102-3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2015.7301270"},{"key":"IJSSMET.2020040102-4","doi-asserted-by":"publisher","DOI":"10.4018\/IJSSMET.2019100105"},{"issue":"22","key":"IJSSMET.2020040102-5","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.","volume":"318","author":"B. E.Bejnordi","year":"2017","journal-title":"Journal of the American Medical Association"},{"key":"IJSSMET.2020040102-6","first-page":"21","article-title":"Dimensionality reduction strategies for cnn-based classification of histopathological images.","author":"S.Cascianelli","year":"2018","journal-title":"International Conference on Intelligent Interactive Multimedia Systems and Services"},{"key":"IJSSMET.2020040102-7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"IJSSMET.2020040102-8","unstructured":"Chopra, S., Balakrishnan, S., & Gopalan, R. (2013, June). Dlid: Deep learning for domain adaptation by interpolating between domains. In ICML workshop on challenges in representation learning (Vol. 2, No. 6). Academic Press."},{"key":"IJSSMET.2020040102-9","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950492"},{"key":"IJSSMET.2020040102-10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40763-5_51"},{"key":"IJSSMET.2020040102-11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40763-5_51"},{"key":"IJSSMET.2020040102-12","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2012.6252544"},{"key":"IJSSMET.2020040102-13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23192-1_40"},{"key":"IJSSMET.2020040102-14","doi-asserted-by":"crossref","unstructured":"Dif, N., walid Attaoui, M., & Elberrichi, Z. (2018, December). Gene Selection for Microarray Data Classification Using Hybrid Meta-Heuristics. Proceedings of theInternational Symposium on Modelling and Implementation of Complex Systems (pp. 119-132). Cham: Springer.","DOI":"10.1007\/978-3-030-05481-6_9"},{"key":"IJSSMET.2020040102-15","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"IJSSMET.2020040102-16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93000-8_86"},{"key":"IJSSMET.2020040102-17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-46466-9_18"},{"key":"IJSSMET.2020040102-18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.patcog.2019.04.009","article-title":"Reshaping inputs for convolutional neural network: Some common and uncommon methods.","volume":"93","author":"S.Ghosh","year":"2019","journal-title":"Pattern Recognition"},{"key":"IJSSMET.2020040102-19","first-page":"221","article-title":"A Comparative Study of 2 Resolution-Level LBP Descriptors and Compact Versions for Visual Analysis","author":"K.Hammoudi","year":"2018","journal-title":"Advanced Multimedia and Ubiquitous Engineering"},{"key":"IJSSMET.2020040102-20","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/978-3-030-00949-6_3","article-title":"Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network","author":"Y.Huang","year":"2018","journal-title":"Computational Pathology and Ophthalmic Medical Image Analysis"},{"key":"IJSSMET.2020040102-21","doi-asserted-by":"publisher","DOI":"10.1002\/cne.902330203"},{"key":"IJSSMET.2020040102-22","unstructured":"Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift."},{"key":"IJSSMET.2020040102-23","article-title":"Three-class mammogram classification based on descriptive CNN features.","author":"M. M.Jadoon","year":"2017","journal-title":"BioMed Research International"},{"key":"IJSSMET.2020040102-24","first-page":"7","article-title":"Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.","author":"A.Janowczyk","year":"2016","journal-title":"Journal of Pathology Informatics"},{"key":"IJSSMET.2020040102-25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11179-7_34"},{"issue":"1","key":"IJSSMET.2020040102-26","doi-asserted-by":"crossref","first-page":"e1002730","DOI":"10.1371\/journal.pmed.1002730","article-title":"Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.","volume":"16","author":"J. N.Kather","year":"2019","journal-title":"PLoS Medicine"},{"key":"IJSSMET.2020040102-27","doi-asserted-by":"publisher","DOI":"10.1038\/srep27988"},{"key":"IJSSMET.2020040102-28","doi-asserted-by":"crossref","first-page":"27988","DOI":"10.1038\/srep27988","article-title":"Multi-class texture analysis in colorectal cancer histology.","volume":"6","author":"J. N.Kather","year":"2016","journal-title":"Scientific Reports"},{"key":"IJSSMET.2020040102-29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.03.022"},{"key":"IJSSMET.2020040102-30","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). Academic Press."},{"key":"IJSSMET.2020040102-31","first-page":"1","article-title":"A comparative study of CNN, BoVW and LBP for classification of histopathological images.","author":"M. D.Kumar","year":"2017","journal-title":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)"},{"key":"IJSSMET.2020040102-32","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., & Zhao, J. (2015, February). Recurrent convolutional neural networks for text classification. Proceedings of theTwenty-ninth AAAI conference on artificial intelligence. AAAI.","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"IJSSMET.2020040102-33","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"IJSSMET.2020040102-34","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"IJSSMET.2020040102-35","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.01.010"},{"key":"IJSSMET.2020040102-36","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2017.8309056"},{"key":"IJSSMET.2020040102-37","unstructured":"Malik, J., Kiranyaz, S., Kunhoth, S., Ince, T., Al-Maadeed, S., Hamila, R., & Gabbouj, M. (2019). Colorectal cancer diagnosis from histology images: A comparative study."},{"key":"IJSSMET.2020040102-38","doi-asserted-by":"publisher","DOI":"10.1016\/j.icte.2018.10.007"},{"key":"IJSSMET.2020040102-39","doi-asserted-by":"publisher","DOI":"10.1145\/2818346.2830593"},{"key":"IJSSMET.2020040102-40","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2016.2520880"},{"key":"IJSSMET.2020040102-41","doi-asserted-by":"publisher","DOI":"10.1109\/38.946629"},{"key":"IJSSMET.2020040102-42","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2018.03.001"},{"key":"IJSSMET.2020040102-43","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-008-0380-5"},{"key":"IJSSMET.2020040102-44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-42999-1_8"},{"key":"IJSSMET.2020040102-45","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition."},{"key":"IJSSMET.2020040102-46","doi-asserted-by":"publisher","DOI":"10.4018\/IJSSMET.2019070105"},{"key":"IJSSMET.2020040102-47","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-016-1318-9"},{"key":"IJSSMET.2020040102-48","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2496264"},{"key":"IJSSMET.2020040102-49","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017, February). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of theThirty-First AAAI Conference on Artificial Intelligence. AAAI Press.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"IJSSMET.2020040102-50","first-page":"1","article-title":"Going deeper with convolutions.","author":"C.Szegedy","year":"2015","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"IJSSMET.2020040102-51","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision.","author":"C.Szegedy","year":"2016","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"IJSSMET.2020040102-52","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535302"},{"key":"IJSSMET.2020040102-53","first-page":"210","article-title":"Rotation equivariant CNNs for digital pathology.","author":"B. S.Veeling","year":"2018","journal-title":"International Conference on Medical image computing and computer-assisted intervention"},{"key":"IJSSMET.2020040102-54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93000-8_92"},{"key":"IJSSMET.2020040102-55","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ins.2018.12.089","article-title":"Classification of breast cancer histology images using incremental boosting convolution networks.","volume":"482","author":"D. M.Vo","year":"2019","journal-title":"Information Sciences"},{"key":"IJSSMET.2020040102-56","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2017.8037745"},{"key":"IJSSMET.2020040102-57","first-page":"95","article-title":"Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease","author":"J. M.Wolterink","year":"2016","journal-title":"Reconstruction, segmentation, and analysis of medical images"},{"key":"IJSSMET.2020040102-58","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2458702"},{"key":"IJSSMET.2020040102-59","unstructured":"Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems (pp. 3320-3328). Academic Press."},{"key":"IJSSMET.2020040102-60","doi-asserted-by":"crossref","unstructured":"Zeiler, M. D., & Fergus, R. (2014, September). Visualizing and understanding convolutional networks. Proceedings of theEuropean conference on computer vision (pp. 818-833). Cham: Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"IJSSMET.2020040102-61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70093-9_71"}],"container-title":["International Journal of Service Science, Management, Engineering, and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=248498","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T23:03:22Z","timestamp":1665788602000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJSSMET.2020040102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2020,4]]},"references-count":62,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.4018\/ijssmet.2020040102","relation":{},"ISSN":["1947-959X","1947-9603"],"issn-type":[{"type":"print","value":"1947-959X"},{"type":"electronic","value":"1947-9603"}],"subject":[],"published":{"date-parts":[[2020,4]]}}}