Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
Vaswani, A. et al. Attention is all you need. In Advances in neural information processing systems 30 (NeurIPS, 2017).
Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).
Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).
Baxi, V., Edwards, R., Montalto, M. & Saha, S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod. Pathol. 35, 23–32 (2022).
Tizhoosh, H. R. & Pantanowitz, L. Artificial intelligence and digital pathology: challenges and opportunities. J. Pathol. Inf. 9, 38 (2018).
Pantanowitz, L. et al. Twenty years of digital pathology: an overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives. J. Pathol. Inf. 9, 40 (2018).
Colling, R. et al. Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J. Pathol. 249, 143–150 (2019).
Acs, B., Rantalainen, M. & Hartman, J. Artificial intelligence as the next step towards precision pathology. J. Intern. Med. 288, 62–81 (2020).
Srinidhi, C. L., Ciga, O. & Martel, A. L. Deep neural network models for computational histopathology: A survey. Med. Image Anal. 67, 101813 (2021).
Niazi, M. K. K., Parwani, A. V. & Gurcan, M. N. Digital pathology and artificial intelligence. Lancet Oncol. 20, e253–e261 (2019).
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16, 703–715 (2019).
Ehteshami Bejnordi, B. et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA 318, 2199–2210 (2017).
Lu, M. Y. et al. AI-based pathology predicts origins for cancers of unknown primary. Nature 594, 106–110 (2021).
Wulczyn, E. et al. Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digital Med. 4, 71 (2021).
Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1, 800–810 (2020).
Thakur, N., Yoon, H. & Chong, Y. Current trends of artificial intelligence for colorectal cancer pathology image analysis: a systematic review. Cancers 12, 1884 (2020).
Krithiga, R. & Geetha, P. Breast cancer detection, segmentation and classification on histopathology images analysis: a systematic review. Arch. Comput. Methods Eng. 28, 2607–2619 (2021).
Allaume, P. et al. Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review. Diagnostics 13, 1799 (2023).
Clarke, E. L., Wade, R. G., Magee, D., Newton-Bishop, J. & Treanor, D. Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis. Sci. Rep. 13, 4774 (2023).
Girolami, I. et al. Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review. J. Nephrol. 35, 1801–1808 (2022).
Rodriguez, J. P. M. et al. Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: a systematic review. J. Pathol. Inform. 13, 100138 (2022).
Parikh, R. B., Teeple, S. & Navathe, A. S. Addressing bias in artificial intelligence in health care. JAMA 322, 2377–2378 (2019).
Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digital Med. 5, 48 (2022).
Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 368, m689 (2020).
The Royal College of Pathologists. Meeting pathology demand – Histopathology workforce census 2017/2018 (The Royal College of Pathologists, 2018).
The Royal College of Pathologists. Position statement from the Royal College of Pathologists (RCPath) on Digital Pathology and Artificial Intelligence (AI) (The Royal College of Pathologists, 2023).
Litjens, G. et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016).
Iizuka, O. et al. Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours. Sci. Rep. 10, 1504 (2020).
Yan, J., Chen, H., Li, X. & Yao, J. Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis. Comput. Med. Imaging Graph 97, 102053 (2022).
Xu, Y., Jiang, L., Huang, S., Liu, Z. & Zhang, J. Dual resolution deep learning network with self-attention mechanism for classification and localisation of colorectal cancer in histopathological images. J. Clin. Pathol. 76, 524–530 (2022).
Wang, S. et al. RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification. Med. Image Anal. 58, 101549 (2019).
Wang, C., Shi, J., Zhang, Q. & Ying, S. Histopathological image classification with bilinear convolutional neural networks. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 4050–4053 (IEEE, 2017).
Tung, C. L. et al. Identifying pathological slices of gastric cancer via deep learning. J. Formos. Med. Assoc. 121, 2457–2464 (2022).
Tsuneki, M. & Kanavati, F. Deep learning models for poorly differentiated colorectal adenocarcinoma classification in whole slide images using transfer learning. Diagnostics 11, 2074 (2021).
Steinbuss, G., Kriegsmann, K. & Kriegsmann, M. Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies. Int. J. Mol. Sci. 21, 6652 (2020).
Song, Z. et al. Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists. BMJ Open 10, e036423 (2020).
Rasmussen, S., Arnason, T. & Huang, W. Y. Deep learning for computer assisted diagnosis of hereditary diffuse gastric cancer. Mod. Pathol. 33, 755–756 (2020).
Cho, K. O., Lee, S. H. & Jang, H. J. Feasibility of fully automated classification of whole slide images based on deep learning. Korean J. Physiol. Pharmacol. 24, 89–99 (2020).
Ashraf, M., Robles, W. R. Q., Kim, M., Ko, Y. S. & Yi, M. Y. A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network. Sci. Rep. 12, 1392 (2022).
Wang, K. S. et al. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Med. 19, 76 (2021).
Wu, W. et al. MLCD: A Unified Software Package for Cancer Diagnosis. JCO Clin. Cancer Inf. 4, 290–298 (2020).
Wang, Q., Zou, Y., Zhang, J. & Liu, B. Second-order multi-instance learning model for whole slide image classification. Phys. Med. Biol. 66, 145006 (2021).
Kanavati, F., Ichihara, S. & Tsuneki, M. A deep learning model for breast ductal carcinoma in situ classification in whole slide images. Virchows Arch. 480, 1009–1022 (2022).
Jin, Y. W., Jia, S., Ashraf, A. B. & Hu, P. Integrative data augmentation with u-net segmentation masks improves detection of lymph node metastases in breast cancer patients. Cancers 12, 1–13 (2020).
Hameed, Z., Zahia, S., Garcia-Zapirain, B., Javier Aguirre, J. & María Vanegas, A. Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models. Sensors, 20, 4373 (2020).
Choudhary, T., Mishra, V., Goswami, A. & Sarangapani, J. A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices. Comput. Biol. Med. 134, 104432 (2021).
Cengiz, E., Kelek, M. M., Oğuz, Y. & Yılmaz, C. Classification of breast cancer with deep learning from noisy images using wavelet transform. Biomed. Tech. 67, 143–150 (2022).
Zhu, M. et al. Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides. Sci. Rep. 11, 7080 (2021).
Tsuneki, M., Abe, M. & Kanavati, F. A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning. Diagnostics 12, 768 (2022).
Swiderska-Chadaj, Z. et al. Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer. Sci. Rep. 10, 14398 (2020).
Han, W. et al. Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens. J. Med. Imaging 7, 047501 (2020).
Fenstermaker, M., Tomlins, S. A., Singh, K., Wiens, J. & Morgan, T. M. Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation. Urology 144, 152–157 (2020).
Esteban, A. E. et al. A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes. Comput. Methods Prog. Biomed. 178, 303–317 (2019).
da Silva, L. M. et al. Independent real-world application of a clinical-grade automated prostate cancer detection system. J. Pathol. 254, 147–158 (2021).
Zhao, L. et al. Lung cancer subtype classification using histopathological images based on weakly supervised multi-instance learning. Phys. Med. Biol. 66, 235013 (2021).
Zhang, X. et al. Deep Learning of Rhabdomyosarcoma Pathology Images for Classification and Survival Outcome Prediction. Am. J. Pathol. 192, 917–925 (2022).
Wang, X. et al. Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis. IEEE Trans. Cyber. 50, 3950–3962 (2020).
Wang, L. et al. Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning. Br. J. Ophthalmol. 104, 318–323 (2020).
Sun, H., Zeng, X., Xu, T., Peng, G. & Ma, Y. Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms. IEEE J. Biomed. Health Inf. 24, 1664–1676 (2020).
Song, J. W., Lee, J. H., Choi, J. H. & Chun, S. J. Automatic differential diagnosis of pancreatic serous and mucinous cystadenomas based on morphological features. Comput. Biol. Med. 43, 1–15 (2013).
Shin, S. J. et al. Style transfer strategy for developing a generalizable deep learning application in digital pathology. Comput. Methods Prog. Biomed. 198, 105815 (2021).
Schau, G. F. et al. Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin. J. Med. Imaging 7, 012706 (2020).
Naito, Y. et al. A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy. Sci. Rep. 11, 8454 (2021).
Mohlman, J., Leventhal, S., Pascucci, V. & Salama, M. Improving augmented human intelligence to distinguish burkitt lymphoma from diffuse large B-cell lymphoma cases. Am. J. Clin. Pathol. 152, S122 (2019).
Miyoshi, H. et al. Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma. Lab. Invest. 100, 1300–1310 (2020).
Li, Y. et al. Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning. Artif. Intell. Med. 108, 101918 (2020).
Li, X., Cheng, H., Wang, Y. & Yu, J. Histological subtype classification of gliomas in digital pathology images based on deep learning approach. J. Med. Imaging Health Inform. 8, 1422–1427 (2018).
Kanavati, F. et al. Weakly-supervised learning for lung carcinoma classification using deep learning. Sci. Rep. 10, 9297 (2020).
Höhn, J. et al. Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification. Eur. J. Cancer 149, 94–101 (2021).
Hekler, A. et al. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur. J. Cancer 118, 91–96 (2019).
Fu, H. et al. Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks. Front. Oncol. 11, 665929 (2021).
De Logu, F. et al. Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm. Front. Oncol. 10, 1559 (2020).
Achi, H. E. et al. Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning. Ann. Clin. Lab. Sci. 49, 153–160 (2019).
Aatresh, A. A., Alabhya, K., Lal, S., Kini, J. & Saxena, P. U. P. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images. Int. J. Comput. Assist. Radio. Surg. 16, 1549–1563 (2021).
Aggarwal, R. et al. Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis. NPJ Digital Med. 4, 1–23 (2021).
Xiao, C., Choi, E. & Sun, J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J. Am. Med. Inform. Assoc. 25, 1419–1428 (2018).
Loftus, T. J. et al. Artificial intelligence and surgical decision-making. JAMA Surg. 155, 148–158 (2020).
Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51, 12–18 (2019).
Zhang, S. et al. Deep learning in human activity recognition with wearable sensors: A review on advances. Sensors 22, 1476 (2022).
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today 23, 1241–1250 (2018).
Ailia, M. J. et al. Current trend of artificial intelligence patents in digital pathology: a systematic evaluation of the patent landscape. Cancers 14, 2400 (2022).
Pantanowitz, L. et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digital Health 2, e407–e416 (2020).
Liu, X. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digital Health 1, e271–e297 (2019).
Roberts, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021).
Song, Z. et al. Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning. Nat. Commun. 11, 4294 (2020).
Sounderajah, V. et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 11, e047709 (2021).
Alheejawi, S., Berendt, R., Jha, N., Maity, S. P. & Mandal, M. Detection of malignant melanoma in H&E-stained images using deep learning techniques. Tissue Cell 73, 101659 (2021).
Noorbakhsh, J. et al. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat. Commun. 11, 6367 (2020).
Salameh, J.-P., et al. Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist. BMJ 370, m2632 (2020).
Sounderajah, V. et al. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat. Med. 27, 1663–1665 (2021).
Whiting, P. F. et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 155, 529–536 (2011).
McGuinness, L. A. & Higgins, J. P. Risk‐of‐bias VISualization (robvis): an R package and Shiny web app for visualizing risk‐of‐bias assessments. Res. Synth. Methods 12, 55–61 (2021).
Patel, A., Cooper, N., Freeman, S. & Sutton, A. Graphical enhancements to summary receiver operating characteristic plots to facilitate the analysis and reporting of meta‐analysis of diagnostic test accuracy data. Res. Synth. Methods 12, 34–44 (2021).
Cruz-Roa, A. et al. High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. PLoS One 13, e0196828 (2018).
Cruz-Roa, A. et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. Sci. Rep. 7, 46450 (2017).
Johny, A. & Madhusoodanan, K. N. Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images. Comput. Math. Methods Med. 2021, 5557168 (2021).
Khalil, M. A., Lee, Y. C., Lien, H. C., Jeng, Y. M. & Wang, C. W. Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis. Diagnostics 12, 990 (2022).
Lin, H. et al. Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection. IEEE Trans. Med. Imaging 38, 1948–1958 (2019).
Roy, S. D., Das, S., Kar, D., Schwenker, F. & Sarkar, R. Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features. Sensors 21, 3628 (2021).
Sadeghi, M. et al. Feedback-based Self-improving CNN Algorithm for Breast Cancer Lymph Node Metastasis Detection in Real Clinical Environment. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2019, 7212–7215 (2019).
Steiner, D. F. et al. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. Am. J. Surg. Pathol. 42, 1636–1646 (2018).
Valkonen, M. et al. Metastasis detection from whole slide images using local features and random forests. Cytom. A 91, 555–565 (2017).
Chen, C. L. et al. An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nat. Commun. 12, 1193 (2021).
Chen, Y. et al. A whole-slide image (WSI)-based immunohistochemical feature prediction system improves the subtyping of lung cancer. Lung Cancer 165, 18–27 (2022).
Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).
Dehkharghanian, T. et al. Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma. Am. J. Pathol. 191, 2172–2183 (2021).
Wei, J. W. et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci. Rep. 9, 3358 (2019).
Yang, H. et al. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study. BMC Med. 19, 80 (2021).
Zheng, Y. et al. A Graph-Transformer for Whole Slide Image Classification. IEEE Trans. Med. Imaging 41, 3003–3015 (2022).
Uegami, W. et al. MIXTURE of human expertise and deep learning-developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease. Mod. Pathol. 35, 1083–1091 (2022).
Kimeswenger, S. et al. Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns. Mod. Pathol. 34, 895–903 (2021).
Li, T. et al. Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study. J. Health. Eng. 2021, 5972962 (2021).
Del Amor, R. et al. An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images. Artif. Intell. Med. 121, 102197 (2021).
Chen, M. et al. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis. Oncol. 4, 1–7 (2020).
Kiani, A., et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. Nat. Res. 3, 23 (2020).
Yang, T. L. et al. Pathologic liver tumor detection using feature aligned multi-scale convolutional network. Artif. Intell. Med. 125, 102244 (2022).
Sali, R. et al. Deep learning for whole-slide tissue histopathology classification: A comparative study in the identification of dysplastic and non-dysplastic barrett’s esophagus. J. Personalized Med. 10, 1–16 (2020).
Syed, S. et al. Artificial Intelligence-based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection. J. Pediatr. Gastroenterol. Nutr. 72, 833–841 (2021).
Nasir-Moin, M. et al. Evaluation of an Artificial Intelligence-Augmented Digital System for Histologic Classification of Colorectal Polyps. JAMA Netw. Open 4, e2135271 (2021).
Wei, J. W. et al. Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides. JAMA Netw. Open 3, e203398 (2020).
Feng, R. et al. A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification. IEEE J. Biomed. Health Inform. 25, 3700–3708 (2021).
Haryanto, T., Suhartanto, H., Arymurthy, A. M. & Kusmardi, K. Conditional sliding windows: An approach for handling data limitation in colorectal histopathology image classification. Inform. Med. Unlocked 23, 100565 (2021).
Sabol, P. et al. Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images. J. Biomed. Inf. 109, 103523 (2020).
Schrammen, P. L. et al. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J. Pathol. 256, 50–60 (2022).
Zhou, C. et al. Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning. Comput. Med. Imaging Graph. 88, 101861 (2021).
Ma, B. et al. Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach. Front. Pharmacol. 11, 572372 (2020).
Ba, W. et al. Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm. Chin. Med. Sci. J. 36, 204–209 (2021).
Duran-Lopez, L. et al. Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems. Comput. Biol. Med. 136, 104743 (2021).
Han, W. et al. Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens. Sci. Rep. 10, 9911 (2020).
Huang, W. et al. Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification. JAMA Netw. Open 4, e2132554 (2021).
Abdeltawab, H. et al. A pyramidal deep learning pipeline for kidney whole-slide histology images classification. Sci. Rep. 11, 20189 (2021).
Tabibu, S., Vinod, P. K. & Jawahar, C. V. Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning. Sci. Rep. 9, 10509 (2019).
BenTaieb, A., Li-Chang, H., Huntsman, D. & Hamarneh, G. A structured latent model for ovarian carcinoma subtyping from histopathology slides. Med. Image Anal. 39, 194–205 (2017).
Yu, K. H. et al. Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks. BMC Med. 18, 236 (2020).
Syrykh, C. et al. Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning. NPJ Digital Med. 3, 63 (2020).
Yu, K. H. et al. Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J. Am. Med Inf. Assoc. 27, 757–769 (2020).
Yu, W. H., Li, C. H., Wang, R. C., Yeh, C. Y. & Chuang, S. S. Machine learning based on morphological features enables classification of primary intestinal t-cell lymphomas. Cancers 13, 5463 (2021).
Xu, Y. et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinforma. 18, 281 (2017).
DiPalma, J., Suriawinata, A. A., Tafe, L. J., Torresani, L. & Hassanpour, S. Resolution-based distillation for efficient histology image classification. Artif. Intell. Med. 119, 102136 (2021).
Menon, A., Singh, P., Vinod, P. K. & Jawahar, C. V. Exploring Histological Similarities Across Cancers From a Deep Learning Perspective. Front. Oncol. 12, 842759 (2022).
Schilling, F. et al. Digital pathology imaging and computer-aided diagnostics as a novel tool for standardization of evaluation of aganglionic megacolon (Hirschsprung disease) histopathology. Cell Tissue Res. 375, 371–381 (2019).
Mishra, R., Daescu, O., Leavey, P., Rakheja, D. & Sengupta, A. Convolutional Neural Network for Histopathological Analysis of Osteosarcoma. J. Comput. Biol. 25, 313–325 (2018).
University of Leeds. Virtual Pathology at the University of Leeds. https://www.virtualpathology.leeds.ac.uk/ (2024).
Haddaway, N. R., Page, M. J., Pritchard, C. C. & McGuinness, L. A. PRISMA2020: An R package and Shiny app for producing PRISMA 2020‐compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Syst. Rev. 18, e1230 (2022).