Cited by 940AI in cancer detectionRadiomics and Machine Learning in Medical ImagingCell Image Analysis Techniques

The DOI matches its Crossref record; 108 of the 111 references it cites were found in a registry or an index.

Machine Learning Methods for Histopathological Image Analysis

Open access

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

DOI 10.1016/j.csbj.2018.01.001PMC PMC6158771

Open this paper in CoChat — free account

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.csbj.2018.01.001

Cites

108 of 108 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

ImageNet classification with deep convolutional neural networks
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
Miyato, T., Maeda, S., Koyama, M., & Ishii, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858821 · Cited by 2,860
Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620
Bilinear CNN Models for Fine-Grained Visual Recognition
Lin, T., RoyChowdhury, A., & Maji, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.170 · Cited by 2,234

Flagged first, then by citations

Show all 111 references

3 of the 111 references couldn’t be matched to a known paper.

Research works printed but not linked: 5

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.

Sorted by citations

Items 801–900 of 940

Concordância entre inteligência artificial e uropatologista expert na detecção e graduação histológica do câncer de próstata em espécimes de prostatectomia radical
Melo, P. A. D. S. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2023.tde-01032024-164110
Improving Weather Forecasts for Sailing Events Using a Combination of a Numerical Forecast Model and Machine Learning Postprocessing
Beimel, S., Suari, Y., & Gabbay, F. (2024) · Applied Sciences · doi:10.3390/app14072950
Additional file 3 of Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer
Rączkowska, A., Paśnik, I., Kukiełka, M., Nicoś, M., Budzinska, M. A., Kucharczyk, T., Szumiło, J., Krawczyk, P. A., Crosetto, N., & Szczurek, E. (2022) · Figshare · doi:10.6084/m9.figshare.21184206
Additional file 2 of Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer
Rączkowska, A., Paśnik, I., Kukiełka, M., Nicoś, M., Budzinska, M. A., Kucharczyk, T., Szumiło, J., Krawczyk, P. A., Crosetto, N., & Szczurek, E. (2022) · Figshare · doi:10.6084/m9.figshare.21184203
Instance Segmentation and Classification of Histology Images Using a Multi-task Learning Strategy Based on Cross-stitch and Self-attentionPreprint
Shamsaddini-Farsangi, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4789742
Accurate diagnosis achieved via super-resolution whole slide images by pathologists and artificial intelligencePreprint
Wang, K., Liu, R., Chen, Y., Wang, Y., Gao, Y., Qiu, Y., Zhou, M., Bai, B., Zhang, M., Sun, K., et al. (2024) · medRxiv · doi:10.1101/2024.07.05.24310022
Machine Learning Algorithm for Cancer Prediction: A Bibliometric Review
Senior, I. A. A., Abdulai, M., Karikari, M., Twumasi, L., Ampofo, I. A. J., & Ampofo, B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-66428-1_43
Deep Learning Technique for MRI-Based Brain Tumor Classification
Zhang, Z. (2024) · Transactions on Computer Science and Intelligent Systems Research · doi:10.62051/n521xr37
Comparison of DenseNet201, InceptionV3, and their fusion models in breast cancer tissue image classification
Ge, R., Chen, G., Saruta, K., & Terata, Y. (2024) · International Conference on Digital Image Processing · doi:10.1117/12.3037276
Enhanced Computer-aided Digital Imaging Technique for Predictions in Breast Cancer
Nagdeote, S., Prabhu, S., & Chaudhari, J. P. (2024) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965282930240417113057
Breast Cancer Detection with Optimized Machine-Learning Techniques
Shahapurkar, V., Dhamdhere, V., & Ansari, N. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6681-9_24
Preanalytic factors in digital pathology implementation
Ntiamoah, P., Ayob, G., Beckles, D., Serrette, R., & Castro, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13809-6.00006-3
The Evolving Nature of the Tumor Immune Microenvironment
Vallejo-Ardila, D. L., & Bastiani, M. A. D. (2023) · Springer eBooks · doi:10.1007/978-3-030-80962-1_369-1
Generative deep learning in digital pathology
MORRISON, D. L., Harris‐Birtill, D., & Caie, P. D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95359-7.00013-3
An Ensemble Technique for Classification of Oral Cancer by Using Histopathological Imaging
Saikia, T., Dhamaniya, A., Gupta, P., & Singh, K. K. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-4359-9_66
Breast Cancer Diagnosis with Histopathology Images using Adaptive Feature Extraction and Machine Learning Model
Sridurgesh, R., & Singh, A. R. (2024) · International Conference on Data Intelligence and Cognitive Informatics (ICDICI) · doi:10.1109/icdici62993.2024.10810857
Histopathological Image Classification Using Fine-Tuned Deep Learning Models for Lung and Colon Cancer
Shah, S., Mahajan, A., Sharma, P., & Sharma, S. K. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-8096-9_17
Automated Breast Cancer Detection: A Review
Alkhater, R. H. M., & Al-Maadeed, S. A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-82156-1_23
Research on image processing based on machine learning feature fusion and sparse representation
Chen, A. (2025) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2025-0338
A Comprehensive Survey on Diagnostic Microscopic Imaging Modalities, Challenges, Taxonomy, and Future Directions for Cervical Abnormality Detection and Grading
Mohanta, A., Roy, S. D., Nath, N., Datta, A., & Bhowmik, M. K. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3551669 · Cited by 1
An enhanced framework for detecting the histopathology cancer using machine learning technique
Gotlur, K., Padmanathan, R. R., Polepaka, S., Vanam, M., Subramanian, S. S., Negi, G. S., & Bhalla, L. (2025) · AIP conference proceedings · doi:10.1063/5.0261563
Lung Cancer Detection Using Deep-Learning Models
Semwal, P., Saini, R., Singh, S., & Mittal, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-88039-1_29
Dynamic AI Tissue Analysis For Enhanced Medical Diagnosis using TSDM
Kanimozhi, J. K., Jtlin Marya, J., Kandasamy, C. A., Prakash, N., Yoganathan, A. P., & Venkatesan, M. (2025) · International Conference on Smart Technologies, Communication and Robotics (STCR) · doi:10.1109/stcr62650.2025.11019002
Pathological Image Segmentation of Breast Cancer via Template Matching
Yan, K., Liu, Y., Li, J., Cao, J., & Zhou, D. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3578303
Lymphovascular Invasion Detection in Breast Cancer Using Deep LearningPreprint
Keshvarikhojasteh, H., Stathonikos, N., Pham, P., van Diest, P. J., Vreuls, C. P. H., Bertram, C., Pluim, J. P. W., & Veta, M. (2025) · medRxiv · doi:10.1101/2025.06.07.25329195
Methodology for Identifying the Cancerous Cells in 3D Visualization of Gastric Histopathology Images using CNN LSTM Algorithm
Vasuki, S., G, J. V., Srikamu, K., Deepika, L., Jeyashree, M., & Alagu Udhayashree, R. (2025) · International Conference on Trends in Engineering Systems and Technologies (ICTEST) · doi:10.1109/ictest64710.2025.11042692
Computational Image Analysis
Gagniuc, E. (2025) · Springer eBooks · doi:10.1007/978-3-031-86270-0_3
Adaptive token attribute fitting for enhanced semantic segmentation in whole slide imaging
Zhang, Y., Huang, Y. J., Zhang, G., He, Z., Li, Z., & Wu, L. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105486
OCELOT 2023: Cell detection from cell–tissue interaction challenge
Shin, J., Ryu, J., Puche, A. V., Lee, J., Brattoli, B., Jung, W., Cho, S. I., Paeng, K., Ock, C., Yoo, D., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103751
Deep-learning techniques for cancer detection using histopathological images
Gavade, A. B., Gavade, P. A., Kurade, A., Pol, S. S., Bhagvatula, V. S. P., & Nerli, R. B. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33984-4.00035-3
Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster
Reinbigler, M., Sharma, R. K., Pires, R. P., Brunet, É., Kermarrec, A.-M., & Fetita, C. I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-99872-0_21
An Optimized In-Context Learning Strategy for Large Vision-Language Models Improves Accuracy and Standardization of Pathological Gross Examination and SamplingPreprint
He, D., Jin, L., Mei, X., Jia, M., Wang, Z., Ren, B., & Cao, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5401357
The Evolving Nature of the Tumor Immune Microenvironment
Vallejo-Ardila, D. L., & Bastiani, M. A. D. (2025) · Springer eBooks · doi:10.1007/978-3-032-00761-2_369
Enhancing Cancer Detection: A Deep Learning Approach Using CNNs and Transfer Learning
Jaiswal, T., Sahu, N., Parida, N., Rout, N. K., & Dansana, D. (2025) · International Conference on Next Generation of Green Information and Emerging Technologies (GIET) · doi:10.1109/giet65294.2025.11234770
Benchmarking binary classification of Johne’s disease using a multi-resolution histopathological image dataset
Fraiwan, M., & Hananeh, W. M. (2025) · Cluster Computing · doi:10.1007/s10586-025-05847-3
Cell nucleus segmentation based on SPCNN-CapsUnet for biological adhesion
Yao, H., He, H., Chang, F., He, Y., & Meng, X. (2025) · International Conference on Optics, Electronics, and Communication Engineering (OECE) · doi:10.1117/12.3090231
Optimizing Deep Learning for Bone Cancer Detection: Boosting Diagnostic Accuracy and Efficiency with CNNs
Adiga, S. R., & Zafar Ali Khan, N. (2025) · International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) · doi:10.1109/iccams65118.2025.11234037
Interpretable Artificial Neural Network Models for Predicting Anti-Adalimumab Immune Complex and Serum Drug Level in Crohn’s Disease: A Proof-of-Concept Study
Genaro, L. M., Carron, J., Lourenço, G. J., Nagasako, C. K., Reis, G. F. S. R., Camargo, M. G., Oliveira, P. D. S. P., Lima, C. S. P., Ayrizono, M. D. L. S., de Azevedo, A. T., et al. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17121536
LungCLR: A Two-Stage Framework with Contrastive Pretraining for Low Data Lung Cancer Histopathology Classification
Trivedi, K., Pathak, H. K., Pathak, I., Singh, K. K., Antonakakis, M., & Zervakis, M. E. (2025) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe66822.2025.00091
Uncertainty Quantification for Metaplasia Detection in Gastrointestinal Histopathology Images
Acosta-Velasquez, R., Cano, F., Romero, E., & Gonzalez, F. A. (2025) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim67325.2025.11283194
Additional file 2 of Factors associated with engraftment success of patient-derived xenografts of breast cancer
Lee, J., Lee, G., Park, H. S., Jeong, B.-K., Gong, G., Jeong, J. H., & Lee, H. J. (2024) · Figshare · doi:10.6084/m9.figshare.25459674
Additional file 1 of Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence
Wang, K. S., Yu, G., Xu, C., Meng, X. H., Zhou, J., Zheng, C., Deng, Z., Shang, L., Liu, R., Su, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14267076
Additional file 2 of Using parallel pre-trained types of DCNN model to predict breast cancer with color normalization
Noumah, W. A., Jafar, A., & Al Joumaa, K. (2022) · Figshare · doi:10.6084/m9.figshare.18132307
Additional file 2 of Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system
Dunn, C., Brettle, D. S., Cockroft, M., Keating, E., Revie, C., & Treanor, D. E. (2024) · Figshare · doi:10.6084/m9.figshare.26684877
Additional file 1 of MORPHIOUS: an unsupervised machine learning workflow to detect the activation of microglia and astrocytes
Silburt, J., & Aubert, I. (2022) · Figshare · doi:10.6084/m9.figshare.19092517
Additional file 1 of Factors associated with engraftment success of patient-derived xenografts of breast cancer
Lee, J., Lee, G., Park, H. S., Jeong, B.-K., Gong, G., Jeong, J. H., & Lee, H. J. (2024) · Figshare · doi:10.6084/m9.figshare.25459537
Additional file 1 of Using parallel pre-trained types of DCNN model to predict breast cancer with color normalization
Noumah, W. A., Jafar, A., & Joumaa, K. A. (2022) · Figshare · doi:10.6084/m9.figshare.18132301
Additional file 3 of Factors associated with engraftment success of patient-derived xenografts of breast cancer
Lee, J., Lee, G., Park, H. S., Jeong, B.-K., Gong, G., Jeong, J. H., & Lee, H. J. (2024) · Figshare · doi:10.6084/m9.figshare.26696349
Additional file 1 of Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer
Rączkowska, A., Paśnik, I., Kukiełka, M., Nicoś, M., Budzinska, M. A., Kucharczyk, T., Szumiło, J., Krawczyk, P., Crosetto, N., & Szczurek, E. (2022) · Figshare · doi:10.6084/m9.figshare.21184200
Additional file 1 of Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images
Ameen, Y. A., Badary, D. M., Abonnoor, A. E. I., Hussain, K. F., & Sewisy, A. A. E.-M. (2023) · Figshare · doi:10.6084/m9.figshare.22622723
Additional file 1 of Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system
Dunn, C. M., Brettle, D. S., Cockroft, M., Keating, E., Revie, C., & Treanor, D. E. (2024) · Figshare · doi:10.6084/m9.figshare.26684874
Performance Evaluation of MambaVision in Breast Cancer Detection from Histopathology Images
Zan, H. (2025) · DÜMF Mühendislik Dergisi · doi:10.24012/dumf.1691671
Ewangelia życia wobec niektórych wyzwań związanych z rozwojem technologii biomedycznych i sztucznej inteligencji
Kobak, J. (2025) · Teologia i Moralność · doi:10.14746/tim.2025.40.3.4
Clinical feasibility of spatial transcriptomics using discarded tissue from diagnostic breast biopsiesPreprint
Sheridan, J. P., Sun, Q., Chen, J. G., Wei, Y., Jarzabek, V., Jaishankar, A., Amin, I., Sultan, L., Nalan, T., Mueller, S. B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.30.696765
Leveraging Interpretable AI for Deciphering Signature Histopathologic Patterns
Gehlhausen, J. R., Luyten, S. T., Deng, J., Micevic, G., Cohen, J. M., Damsky, W., Cowper, S. E., McNiff, J. M., & Ko, C. J. (2026) · Journal of Cutaneous Pathology · doi:10.1111/cup.70048
Automatisierung und Digitalisierung im histologischen Labor
Lang, G. (2025) · Springer eBooks · doi:10.1007/978-3-662-71093-7_16
Improving ViT Performance for Colon Cancer Histopathological Image Classification Using Transfer Learning
Belaskri, M., Benomar, M. L., Benazzouz, M., Descombes, X., & Ambrosetti, D. (2025) · International Conference on Intelligent Computer Systems, Data Science and Applications (IC2SDA) · doi:10.1109/ic2sda68097.2025.11331535
Women Breast Histopathology Image Classification for Cancer Detection using ResNet101
Swarnkar, H., & Si, T. (2025) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon65210.2025.11341313
Advancing personalised and precision medicine through artificial intelligence: Current insights and future directions
fatima, G., Buttar, H. S., Parvez, S., KHAN, S. K., Mahdi, A. A., & Raza, A. M. (2025) · Scripta Medica · doi:10.5937/scriptamed56-61459
Automated Classification of Store‐Operated Calcium Entry Activity and Disease Conditions in Murine Skeletal Muscle Images Using Machine Learning
Binesh, F., Pasham, K. V. R., Villani, K. R., Krekora, U., Wei‐LaPierre, L., & Barton, E. R. (2026) · Muscle & Nerve · doi:10.1002/mus.70157
BTdiagAI: A Web‐Deployed Hybrid Framework for Brain Tumor Classification Using Optimized MRI Preprocessing and Deep Learning Fusion
Ahmmed, F., Antik, A. R., Alaly, A., Mehnaj, S., Huda, M. S. A., & Ali, M. A. (2026) · Healthcare Technology Letters · doi:10.1049/htl2.70053
AI-Assisted Histopathological Image Analysis for Automated Gastric Cancer Detection
Mandal, D. K., & Kashyap, S. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-03985-9_22
Classification Enhanced Automated Gastric Histopathology Report Generation
Ajayaraj, S., Pullakhandam, C. A., & Pathinarupothi, R. K. (2025) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaihei65991.2025.11379301
Breast Histopathology Image Classification using ResNet152 for Cancer Detection
Swarnkar, H., & Si, T. (2025) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11410812
From Traditional Scoring to Artificial Intelligence: Evolution and Prospects of Prediction Models for Postoperative Pancreatic Fistula after Pancreaticoduodenectomy
许志仁 (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1631008
Artificial intelligence in lung cancer imaging: A review of framework architectures and computer-aided diagnosis advancements
Tan, S. L., Selvachandran, G., Ding, W., & Kotecha, K. V. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114481
Metaheuristic-Optimized Deep Learning Framework for Multimodal Imaging in Cancer Prognosis and Survival Prediction
PradeepKumar, G. M., & N, M. S. V. (2025) · Asian Conference on Intelligent Technologies (ACOIT) · doi:10.1109/acoit66109.2025.11437139
A patch-intuited dense deep network for classification of breast cancer using microscopic imaging
Singh, J., & Sharma, O. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-11953-z
Enhancing Diagnostic Reliability in Lung Cancer Classification: A Feature-Fusion Ensemble Approach using DenseNet121 and InceptionV3
Akshata, Y., Kalyani, G., & Praneeth, K. (2026) · International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) · doi:10.1109/icauc68182.2026.11441135
Detection of collagen band–associated regions in H&E-stained colonic biopsies of collagenous colitis patients using superpixel-based feature extraction and neural network classification
Kiudelis, V., Petrolis, R., Ramonaitė, R., Jančiauskas, D., Poškienė, L., Kupčinskas, J., Šabanas, P., Čerapaitė-Trušinskienė, R., Meilutytė-Lukauskienė, D., & Kriščiukaitis, A. (2026) · Diagnostic Pathology · doi:10.1186/s13000-026-01783-x
HistoXplaining in Style: Counterfactual Explanations for Histopathology Images using StyleGAN2-ADA
Muñoz, B., Raquel Pezoa, R., & Gutierrez, H. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10709-3
Unsupervised and weakly supervised deep learning methods for computer vision and medical imaging
Sahasrabudhe, M. (2020) · theses.fr (ABES) · doi:10.70675/053d6ac3z8c79z451cz88c8z553669be02c2
Probabilistic models for image processing : applications in vascular surgery
Gangloff, H. (2020) · theses.fr (ABES) · doi:10.70675/db40a7e4z91b2z4e00zbf59z6f97ce7137e3
Brain Tumor Classification Using a Fine-Tuned Enhanced CNN
Alaeddine, H., & Kouka, N. (2025) · IEEE Afro-Mediterranean Conference on Artificial Intelligence (AMCAI) · doi:10.1109/amcai66110.2025.11474320
Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights
de Almeida, A. L. T., dos Santos, A. B. G., & Barreto-Vieira, D. F. (2026) · Journal of Imaging · doi:10.3390/jimaging12040173
A Vision-Retrieval-Augmented Generation Framework for Interpretable Cervical Cytology Screening
Edafetanure-Ibeh, F. T., Edafetanure-Ibeh, O. M., & Ihesiulo, O. C. (2026) · IEEE SoutheastCon · doi:10.1109/southeastcon63549.2026.11476079
Artificial Intelligence-Powered Breast Cancer Prognosis: Optimizing Deep Learning with Image Normalization
Yong, C. Y., Pang, D. J. W., Leong, S. M., & Tan, C. W. (2026) · IEEE International Conference on Computation, Big-Data and Engineering (ICCBE) · doi:10.3390/engproc2026128049
TOPO-HCSAN: Hierarchical Cross-Scale Attention Networks with Topological Regularization
Jialu, S., & Zhongyi, H. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464638
GLDA-GAN: A Global–Local Dual-Branch Aggregation GAN for Unpaired One-to-Many Virtual Staining of Kidney HistopathologyPreprint
Yi, T., Xie, J., Huang, Y., Ruan, B., Xiao, S., Chen, Y., Cai, C., Li, J., & Wei, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6637899
Predicting DNA replication–related protein expression at the nucleus level from HE: A deep learning study with same-specimen paired data
Urata, T., Takeyama, S., Kimura, F., Ohshima, K., Ishii, K., Ji, C., Takagi, G., & Yamaguchi, M. (2026) · Acta Histochemica · doi:10.1016/j.acthis.2026.152334
Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis
Reigle, J. K., Liu, X., Lopez‐Nunez, O. F., Drysdale, E., Abuquteish, D., Walters, T. D., Mack, D., Boyle, B. M., Kugathasan, S., Denson, N. K., et al. (2026) · Clinical and Translational Gastroenterology · doi:10.14309/ctg.0000000000001031
GRANet-Lite: A lightweight Ghost–Residual Attention Network for breast cancer classification with comprehensive histopathological preprocessing analysis
Ahmmed, F., Rafsan, R. S., Akther, A., & Mohaimen-Bin-Noor (2026) · Smart Health · doi:10.1016/j.smhl.2026.100661 · Cited by 1
AI-driven pathology: Transforming histopathological analysis
Elmeligy, A. O., Haider, Y., Elemam, N. M., & Talaat, I. M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00017-7
Self-Supervised Feature Learning for Invasive Ductal Carcinoma Detection in Histopathological Images
Bhatt, H., Goel, P., Kaur, N., & Tensingh, S. (2026) · World Conference on Computational Science and Technology (WcCST) · doi:10.1109/wccst67302.2026.11495687
RSTMG: A Computationally Efficient Framework for Region Specific Tissue Segmentation of Whole Slide Images
Thulasidharan, P. P., Mary, L., Nath, K., & Sahoo, J. (2026) · International Conference on Emerging Systems and Intelligent Computing (ESIC) · doi:10.1109/esic68176.2026.11495964
A Deep Learning Framework for Inflammatory Cell–Rich Lung Cancer Histopathological Image Analysis
Aiyer, P., R, A. R. T., Kumaar, P., & Raj, R. (2026) · International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) · doi:10.1109/raeeucci67649.2026.11504652
Ligand–Receptor Interaction Combined with Histopathology Improves Glioma Prognostic Model
Gao, L., Zhang, R., Zhu, X., Xu, H. X., Chen, Q., Peng, M., & Liu, J. (2026) · Biomedicines · doi:10.3390/biomedicines14051110
Leveraging dual transfer learning and attention-based pooling for lung and colon cancer histopathological image classification
Das, P., Kumar, R., & Das, D. K. (2026) · Franklin Open · doi:10.1016/j.fraope.2026.100646
Development and validation of a few-shot rapid screening model for gastrointestinal cancers using AGI large vision models
Liu, L., Yin, F., Yang, G., Li, Q., Li, S., Pan, T., Liu, T., Tang, J., Ming, R., Song, Y. Q., et al. (2026) · Computer Science and Information Systems · doi:10.2298/csis251130024l
Prostate gland cancer detection and grade classifying using gleason score-based machine learning approach
Sundaravadivel, P., Isaac, R. A., Premnath, K., & Kumar, C. H. V. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01380-3
Continual Learning for Histopathology Image Classification in Class-Incremental Learning
Wu, Y., Zhao, Y., & Ralescu, A. (2026) · Diagnostics · doi:10.3390/diagnostics16111711
TFEA-Net: Triplet Feature Enhancement Attention Network for Breast Cancer Classification
Agarwal, R., Pratik, S., & Singh, N. H. (2026) · International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT) · doi:10.1109/etaact69135.2026.11541381
Breast Cancer Histology Image Classification using Transfer Learning and Grad-CAM-Based Explainability
Khan, R., Avi, A. M., Mondal, S., & Dipa, S. A. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11545899
MSOR: Multi-Scale Over-Smoothing Regularization for Virtual Staining
Hussain, M. A., Waris, M. A., Akram, M. U., Hassan, A., Butt, W. H., & Khan, M. J. (2026) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai70912.2026.11551969
TransBreast-Net: An interpretable vision transformer ensemble for breast cancer histopathology classification
Uddin, K. M. M., & Adnan, M. A. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2026.100684
MHCAF-Net: Multi-Scale Cross-Attention Fusion for Histological Grading of Invasive Ductal Carcinoma
Cella, M. V. V., de Oliveira, A. C., Filho, C. L. S. S., Quintanilha, D. B. P., Borchartt, T. B., Clímaco, F. G. N., & Almeida, J. D. S. D. A. (2026) · Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2026) · doi:10.5753/sbcas.2026.21032
Automatic Bevacizumab Response Prediction in Ovarian Cancer from Digital Pathology Images via Novel AI-Based Computational Pipeline
Alsaiari, A., Turki, T., & Taguchi, Y. (2026) · Mathematics · doi:10.3390/math14122224
CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training
Ta, P., Venkatesaperumal, S., Ram, K., & Sivaprakasam, M. (2026) · Neuroinformatics · doi:10.1007/s12021-026-09793-2
Beneficial noise as neighborhood uncertainty for few-shot vision–language medical classification
Cao, J., Ya, M., Huang, X., & Tai, Y. (2026) · Signal Processing · doi:10.1016/j.sigpro.2026.110794
Task-Adaptive Multi-Scale Ensemble Framework for Multi-Cancer Diseases Classification
Ali, A. A., Younis, E. M. G., Hassan, H. S., Abohany, A. A., & Anwar, E. M. (2026) · ICEENG International Conference for Innovations in Electronics, EM-Waves, and Communications (IEEC) · doi:10.1109/ieec69628.2026.11584241

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Machine Learning Methods for Histopathological Image Analysis

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks