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Machine Learning Methods for Histopathological Image Analysis

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Komura, Daisuke, Ishikawa, Shumpei·2018·Computational and Structural Biotechnology Journal

DOI 10.1016/j.csbj.2018.01.001PMC PMC6158771

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Abstract

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.

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Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
The cancer genome atlas pan-cancer analysis project
The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Solving the multiple instance problem with axis-parallel rectangles
Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00034-3 · Cited by 2,866
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
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Barry J.D., Fagny M., Paulson J.N., Aerts H., Platig J. and Quackenbush J.: Histopathological image QTL discovery of thyroid autoimmune disease variants. In: BioRxiv. : 2017.
ScanNet: a fast and dense scanning framework for metastatic breast cancer detection from whole-slide images - semantic scholar. https://www.semanticscholar.org/paper/ScanNet-A-Fast-and-Dense-Scanning-Framework-for-Me-Lin-Chen/9484287f4d5d52d10b5d362c462d4d6955655f8e.
MICCAI grand challenge: tumor proliferation assessment challenge (TUPAC16). MICCAI Gd Chall Tumor Prolif Assess Chall TUPAC16. http://tupac.tue-image.nl/.
KimiaPath24: dataset for retrieval and classification in digital pathology. : KIMIA Lab 2017.
Tong S. and Koller D.: Support vector machine active learning with applications to text classification. J Mach Learn Res 2001; 2: 45.

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Guan, X., Wang, Y., Lin, Y., Li, X., & Zhang, Y. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3349866 · Cited by 24
Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning
Kang, J. S., Lee, C., Song, W., Choo, W., Lee, S., Lee, S., Han, Y., Bassi, C., Salvia, R., Marchegiani, G., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-76974-7 · Cited by 26
Automatic whole slide pathology image diagnosis framework via unit stochastic selection and attention fusion
Chen, P., Liang, Y., Shi, X., Yang, L., & Gader, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.04.153 · Cited by 24
Histopathology Image Segmentation Using MobileNetV2 based U-net Model
Kanadath, A., Jothi, J. A. A., & Urolagin, S. (2021) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit51480.2021.9498341 · Cited by 27
Multi stain graph fusion for multimodal integration in pathology
Dwivedi, C., Nofallah, S., Pouryahya, M., Iyer, J. S., Leidal, K., Chung, C., Watkins, T. R., Billin, A. N., Myers, R. P., Abel, J., et al. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00200 · Cited by 24
PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology
Levy, J., Salas, L. A., Christensen, B. C., Sriharan, A., & Vaickus, L. J. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0036 · Cited by 24
Artificial intelligence driven next-generation renal histomorphometry
Santo, B. A., Rosenberg, A. Z., & Sarder, P. (2020) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000598 · Cited by 24
Differentiating Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features from Classic Papillary Thyroid Carcinoma: Analysis of Cytomorphologic Descriptions Using a Novel Machine-Learning Approach
Maleki, S., Zandvakili, A., Gera, S., Khutti, S., Gersten, A., & Khader, S. N. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_25_19 · Cited by 23
Bias reduction in representation of histopathology images using deep feature selection
Bidgoli, A. A., Rahnamayan, S., Dehkharghanian, T., Grami, A., & Tizhoosh, H. R. (2022) · Scientific Reports · doi:10.1038/s41598-022-24317-z · Cited by 26
Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives
Distante, A., Marandino, L., Bertolo, R. G., Ingels, A., Pavan, N., Pecoraro, A., Marchioni, M., Carbonara, U., Erdem, S., Amparore, D., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13132294 · Cited by 28
Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images
Shanmugam, K., & Rajaguru, H. (2023) · Diagnostics · doi:10.3390/diagnostics13203289 · Cited by 23
Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantify Heterogeneity in Tumour Tissues
Tan, X., Su, A., Hajiabadi, H., Tran, M., & Nguyen, Q. H. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0826-5_10 · Cited by 24
Histopathologic Image Processing: A ReviewPreprint
de Matos, J., Britto, A. D. S., Oliveira, L. S., & Koerich, A. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.07900 · Cited by 33
Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images
Li, H., Qin, J., Li, Z., Ouyang, R., Chen, Z., Huang, S., Qin, S., & Huang, Q. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01848-z · Cited by 28
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Sitnik, D., Aralica, G., Hadžija, M., Hadžija, M. P., Pačić, A., Periša, M. M., Manojlović, L., Krstanac, K., Plavetić, A., & Kopriva, I. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102402 · Cited by 22
Generative Deep Learning in Digital Pathology Workflows
MORRISON, D. L., Harris‐Birtill, D., & Caie, P. D. (2021) · American Journal Of Pathology · doi:10.1016/j.ajpath.2021.02.024 · Cited by 23
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El-Hossiny, A. S., Al‐Atabany, W., Hassan, O., Soliman, A. M., & Sami, S. A. (2021) · IEEE Access · doi:10.1109/access.2021.3076158 · Cited by 22
Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images
Moyes, A., Gault, R., Zhang, K., Ming, J., Crookes, D., & Wang, J. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102640 · Cited by 26
Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron
Ng, C. S. W., & Ghahfarokhi, A. J. (2022) · Applied Computing and Geosciences · doi:10.1016/j.acags.2022.100103 · Cited by 23
ResNet for Histopathologic Cancer Detection, the Deeper, the Better?
Wang, Z., Gao, J., Kan, H., Huang, Y., Tang, F., Li, W., & Yang, F. (2023) · Journal of Data Science and Intelligent Systems · doi:10.47852/bonviewjdsis3202744 · Cited by 21
ChatGPT as an aid for pathological diagnosis of cancer
Malik, S., & Zaheer, S. (2023) · Pathology - Research and Practice · doi:10.1016/j.prp.2023.154989 · Cited by 22
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Gul, G., Korejo, I., Hakro, D., Alqahtani, H., Abbasi, A., Babar, M., Rahbi, O. A., & Ali, N. I. (2026) · De Computis · doi:10.3390/computers15010025 · Cited by 18
ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management
Lee, J., Kim, D., & Lee, J. (2019) · Applied Sciences · doi:10.3390/app9112308 · Cited by 21
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Hamida, A. B., Devanne, M., Weber, J., Truntzer, C., Dérangère, V., Ghiringhelli, F., Forestier, G., & Wemmert, C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102407 · Cited by 21
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Lu, H., Lü, Q., Sun, H., Wang, Z., Shi, X., Ding, Y., Ran, X., Pei, J., Pan, Y., & Zhang, Q. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c09320 · Cited by 21
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Adeoye, J., & Su, Y. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000979 · Cited by 21
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Authors: Komura, D., & Ishikawa, S.
Affiliations: Tokyo Medical and Dental University (JP)
Corresponding author: Komura, Daisuke
ORCID: Komura, Daisuke
Ishikawa, Shumpei
Venue: Computational and Structural Biotechnology Journal
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2018-01
Volume: 16
Pages: 34-42
ISSN: 2001-0370 (ISSN-L)
DOI: 10.1016/j.csbj.2018.01.001
PMC: PMC6158771
OpenAlex: W2751723768
Cited by: 937
References: 111
Citations by year: 2026: 95 · 2025: 111 · 2024: 104 · 2023: 122 · 2022: 114 · 2021: 140 · 2020: 146 · 2019: 88 · 2018: 17
FWCI: 11.44
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor, 15 d)
Locations: Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
arXiv (Cornell University) (cc-by)
Funders: JSPS (25710020)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
Keywords: digital histopathology, histopathology image analysis, computer-aided diagnosis, machine learning

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Machine Learning Methods for Histopathological Image Analysis

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