Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (original) (raw)

• Dentro, S. C. et al. Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell 184, 2239–2254 (2021).
  Article CAS PubMed PubMed Central Google Scholar
• Burrell, R. A., McGranahan, N., Bartek, J. & Swanton, C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–345 (2013).
  Article CAS PubMed Google Scholar
• Yates, L. R. & Campbell, P. J. Evolution of the cancer genome. Nat. Rev. Genet. 13, 795–806 (2012).
  Article CAS PubMed PubMed Central Google Scholar
• Rabbie, R., Lau, D., White, R. M. & Adams, D. J. Unraveling the cartography of the cancer ecosystem. Genome Biol. 22, 87 (2021).
  Article PubMed PubMed Central Google Scholar
• Merlo, L. M. F., Pepper, J. W., Reid, B. J. & Maley, C. C. Cancer as an evolutionary and ecological process. Nat. Rev. Cancer 6, 924–935 (2006).
  Article CAS PubMed Google Scholar
• Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).
  Article Google Scholar
• The Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).
• The Cancer Genome Atlas Research Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
• The Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).
• Cancer Genome Atlas Research Network et al. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).
  Article Google Scholar
• Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).
  Article Google Scholar
• Chen, F. et al. Moving pan-cancer studies from basic research toward the clinic. Nat. Cancer 2, 879–890 (2021).
  Article PubMed Google Scholar
• Echle, A. et al. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br. J. Cancer 124, 686–696 (2020).
• Prewitt, J. M. & Mendelsohn, M. L. The analysis of cell images. Ann. N. Y. Acad. Sci. 128, 1035–1053 (1966).
  Article CAS PubMed Google Scholar
• Onji, K. et al. Quantitative analysis of colorectal lesions observed on magnified endoscopy images. J. Gastroenterol. 46, 1382–1390 (2011).
  Article PubMed Google Scholar
• Irshad, H. et al. Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach. J. Pathol. Inform. 4, S12 (2013).
  Article PubMed PubMed Central Google Scholar
• LeCun, Y. et al. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems (ed. Touretzky, D.) Vol. 2 (Morgan-Kaufmann, 1990).
• Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. In Proceedings of the IEEE 2278–2324 (IEEE, 1998).
• Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1, 800–810 (2020).
• Kather, J. N. et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer 1, 789–799 (2020).
  Article CAS PubMed PubMed Central Google Scholar
• Brockmoeller, S. et al. Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer. J. Pathol. 256, 269–281 (2021).
• Wulczyn, E. et al. Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digit. Med. 4, 71 (2021).
  Article PubMed PubMed Central Google Scholar
• Janowczyk, A. & Madabhushi, A. Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7, 29 (2016).
  Article PubMed PubMed Central Google Scholar
• Ozkan, T. A. et al. Interobserver variability in Gleason histological grading of prostate cancer. Scand. J. Urol. 50, 420–424 (2016).
  Article CAS PubMed Google Scholar
• Gurcan, M. N. et al. Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009).
  Article PubMed PubMed Central Google Scholar
• Sui, D. et al. A pyramid architecture-based deep learning framework for breast cancer detection. BioMed Res. Int. 2021, 2567202 (2021).
  Article PubMed PubMed Central Google Scholar
• Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).
  Article CAS PubMed PubMed Central Google Scholar
• Gehrung, M. et al. Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat. Med. 27, 833–841 (2021).
  Article CAS PubMed Google Scholar
• Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5, 555–570 (2021).
• 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).
  Article PubMed PubMed Central Google Scholar
• Lu, M. Y. et al. AI-based pathology predicts origins for cancers of unknown primary. Nature 594, 106–110 (2021).
• Nagpal, K. et al. Publisher Correction: Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit. Med. 2, 113 (2019).
• Bulten, W. et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21, 233–241 (2020).
  Article PubMed Google Scholar
• Ström, P. et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 21, 222–232 (2020).
  Article PubMed Google Scholar
• Balkenhol, M. C. A. et al. Deep learning assisted mitotic counting for breast cancer. Lab. Invest. 99, 1596–1606 (2019).
  Article PubMed Google Scholar
• Veta, M., van Diest, P. J., Jiwa, M., Al-Janabi, S. & Pluim, J. P. W. Mitosis counting in breast cancer: object-level interobserver agreement and comparison to an automatic method. PLoS ONE 11, e0161286 (2016).
  Article PubMed PubMed Central Google Scholar
• Calderaro, J. & Kather, J. N. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut 70, 1183–1193 (2021).
  Article CAS PubMed Google Scholar
• Saillard, C. et al. Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides. Hepatology 72, 2000–2013 (2020).
• Wulczyn, E. et al. Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS ONE 15, e0233678 (2020).
  Article CAS PubMed PubMed Central Google Scholar
• 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).
  Article CAS PubMed Google Scholar
• Heinz, C. N., Echle, A., Foersch, S., Bychkov, A. & Kather, J. N. The future of artificial intelligence in digital pathology—results of a survey across stakeholder groups. Histopathology 80, 1121–1127 (2022).
• Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. & Aerts, H. J. W. L. Artificial intelligence in radiology. Nat. Rev. Cancer 18, 500–510 (2018).
  Article CAS PubMed PubMed Central Google Scholar
• Bera, K., Braman, N., Gupta, A., Velcheti, V. & Madabhushi, A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat. Rev. Clin. Oncol. 19, 132–146 (2021).
• Hughes, J. W. et al. Deep learning evaluation of biomarkers from echocardiogram videos. EBioMedicine 73, 103613 (2021).
  Article CAS PubMed PubMed Central Google Scholar
• Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).
  Article CAS PubMed Google Scholar
• Pagès, F. et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet 391, 2128–2139 (2018).
  Article PubMed Google Scholar
• Kleppe, A. et al. Chromatin organisation and cancer prognosis: a pan-cancer study. Lancet Oncol. 19, 356–369 (2018).
  Article CAS PubMed PubMed Central Google Scholar
• Kather, J. N. et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 16, e1002730 (2019).
  Article PubMed PubMed Central Google Scholar
• Bychkov, D. et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep. 8, 3395 (2018).
  Article PubMed PubMed Central Google Scholar
• Skrede, O.-J. et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 395, 350–360 (2020).
  Article CAS PubMed Google Scholar
• Courtiol, P. et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25, 1519–1525 (2019).
• Mobadersany, P. et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl Acad. Sci. USA 115, E2970–E2979 (2018).
  Article CAS PubMed PubMed Central Google Scholar
• Howard, F. M. et al. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat. Commun. 12, 4423 (2021).
  Article CAS PubMed PubMed Central Google Scholar
• Kleppe, A. et al. Designing deep learning studies in cancer diagnostics. Nat. Rev. Cancer 21, 199–211 (2021).
• Madabhushi, A., Wang, X., Barrera, C. & Velcheti, V. Predicting response to immunotherapy using computer extracted features of cancer nuclei from hematoxylin and eoisin (H&E) stained images of non-small cell lung cancer (NSCLC). US Patent 11055844B2 (2019).
• Farahmand, S. et al. Deep learning trained on hematoxylin and eosin tumor region of interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer. Mod. Pathol. 35, 44–51 (2021).
• Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054–1056 (2019).
  Article CAS PubMed PubMed Central Google Scholar
• Echle, A. et al. Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning. Gastroenterology 159, 1406–1416 (2020).
  Article CAS PubMed Google Scholar
• Yamashita, R. et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol. 22, 132–141 (2021).
  Article PubMed Google Scholar
• Echle, A. et al. Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: a systematic literature review. ImmunoInformatics 3-4, 100008 (2021).
  Article CAS Google Scholar
• Bilal, M., Raza, S. E. A., Azam, A., Graham, S. & Ilyas, M. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit. Health 3, e763–e772 (2021).
• Muti, H. S. et al. Development and validation of deep learning classifiers to detect Epstein–Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. Lancet Digit. Health 3, e654–e664 (2021).
• Hong, R., Liu, W., DeLair, D., Razavian, N. & Fenyö, D. Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Rep. Med. 2, 100400 (2021).
  Article CAS PubMed PubMed Central Google Scholar
• Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
  Article CAS PubMed PubMed Central Google Scholar
• Harder, N. et al. Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma. Sci. Rep. 9, 7449 (2019).
  Article PubMed PubMed Central Google Scholar
• Popovici, V. et al. Joint analysis of histopathology image features and gene expression in breast cancer. BMC Bioinformatics 17, 209 (2016).
  Article PubMed PubMed Central Google Scholar
• Schulz, S. et al. Multimodal deep learning for prognosis prediction in renal cancer. Front. Oncol. 11, 788740 (2021).
  Article PubMed PubMed Central Google Scholar
• Hao, J., Kosaraju, S. C., Tsaku, N. Z., Song, D. H. & Kang, M. PAGE-Net: interpretable and integrative deep learning for survival analysis using histopathological images and genomic data. In Biocomputing 2020 355–366 (World Scientific, 2019).
• Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J. & Shah, S. P. Harnessing multimodal data integration to advance precision oncology. Nat. Rev. Cancer 22, 114–126 (2021).
• Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P. & Kather, J. N. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP Rep. 4, 100443 (2022).
• Greenson, J. K. et al. Pathologic predictors of microsatellite instability in colorectal cancer. Am. J. Surg. Pathol. 33, 126–133 (2009).
  Article PubMed PubMed Central Google Scholar
• Hyde, A. et al. A histology-based model for predicting microsatellite instability in colorectal cancers. Am. J. Surg. Pathol. 34, 1820–1829 (2010).
  Article PubMed Google Scholar
• Couture, H. D. et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer 4, 30 (2018).
  Article PubMed PubMed Central Google Scholar
• Schaumberg, A. J., Rubin, M. A. & Fuchs, T. J. H&E-stained whole slide image deep learning predicts SPOP mutation state in prostate cancer. Preprint at bioRxiv https://doi.org/10.1101/064279 (2017).
• Schrammen, P. L. et al. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J. Pathol. 256, 50–60 (2021).
• Sirinukunwattana, K. et al. Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning. Gut 70, 544–554 (2021).
  Article CAS PubMed Google Scholar
• Bilal, M. et al. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit. Health 3, e763–e772 (2021).
• Schirris, Y., Gavves, E., Nederlof, I., Horlings, H. M. & Teuwen, J. DeepSMILE: self-supervised heterogeneity-aware multiple instance learning for DNA damage response defect classification directly from H&E whole-slide images. Preprint at https://arxiv.org/abs/2107.09405 (2021).
• Schmauch, B. et al. A deep learning model to predict RNA-seq expression of tumours from whole slide images. Nat. Commun. 11, 3877 (2020).
  Article CAS PubMed PubMed Central Google Scholar
• Cao, R. et al. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in colorectal cancer. Theranostics 10, 11080–11091 (2020).
  Article CAS PubMed PubMed Central Google Scholar
• Binder, A. et al. Morphological and molecular breast cancer profiling through explainable machine learning. Nat. Mach. Intell. 3, 355–366 (2021).
  Article Google Scholar
• Yu, K.-H. et al. Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J. Am. Med. Inform. Assoc. 27, 757–769 (2020).
  Article PubMed PubMed Central Google Scholar
• Loeffler, C. M. L. et al. Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: a possible preselection for molecular testing? Eur. Urol. Focus 8, 472–479 (2021).
• Levy-Jurgenson, A., Tekpli, X., Kristensen, V. N. & Yakhini, Z. Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci. Rep. 10, 18802 (2020).
  Article CAS PubMed PubMed Central Google Scholar
• 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).
  Article CAS PubMed Google Scholar
• AbdulJabbar, K. et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat. Med. 26, 1054–1062 (2020).
  Article CAS PubMed PubMed Central Google Scholar
• Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
  Article PubMed Google Scholar
• Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 2419 (2018).
  Article PubMed PubMed Central Google Scholar
• Erickson, A. et al. The spatial landscape of clonal somatic mutations in benign and malignant tissue. Preprint at bioRxiv https://doi.org/10.1101/2021.07.12.452018 (2021).
• Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53, 1334–1347 (2021).
  Article CAS PubMed PubMed Central Google Scholar
• Thrane, K., Eriksson, H., Maaskola, J., Hansson, J. & Lundeberg, J. Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma. Cancer Res. 78, 5970–5979 (2018).
  Article CAS PubMed Google Scholar
• Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022).
  Article CAS PubMed Google Scholar
• Lomakin, A. et al. Spatial genomics maps the structure, character and evolution of cancer clones. Preprint at bioRxiv https://doi.org/10.1101/2021.04.16.439912 (2021).
• Wang, D., Khosla, A., Gargeya, R. & Irshad, H. Deep learning for identifying metastatic breast cancer. Preprint at https://arxiv.org/abs/1606.05718 (2016).
• Gecer, B. et al. Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recognit. 84, 345–356 (2018).
  Article PubMed PubMed Central Google Scholar
• Shaban, M. et al. Context-aware convolutional neural network for grading of colorectal cancer histology images. IEEE Trans. Med. Imaging 39, 2395–2405 (2020).
  Article PubMed Google Scholar
• Ciga, O., Xu, T. & Martel, A. L. Self supervised contrastive learning for digital histopathology. Mach. Learn. Appl. 7, 100198 (2022).
• Gadermayr, M. et al. Generative adversarial networks for facilitating stain-independent supervised and unsupervised segmentation: a study on kidney histology. IEEE Trans. Med. Imaging 38, 2293–2302 (2019).
  Article PubMed Google Scholar
• de Bel, T., Hermsen, M., Kers, J., van der Laak, J. & Litjens, G. Stain-transforming cycle-consistent generative adversarial networks for improved segmentation of renal histopathology. International Conference on Medical Imaging with Deep Learning https://openreview.net/forum?id=BkxJkgSlx4 (2018).
• Xu, J. et al. Stacked Sparse Autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35, 119–130 (2016).
  Article PubMed Google Scholar
• Noroozi, M. & Favaro, P. Unsupervised learning of visual representations by solving jigsaw puzzles. In Computer Vision—ECCV 2016 69–84 (Springer Nature, 2016).
• Srinidhi, C. L., Ciga, O. & Martel, A. L. Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021).
  Article PubMed Google Scholar
• Koohbanani, N. A., Unnikrishnan, B., Khurram, S. A., Krishnaswamy, P. & Rajpoot, N. Self-Path: self-supervision for classification of pathology images with limited annotations. IEEE Trans. Med. Imaging 40, 2845–2856 (2021).
  Article PubMed Google Scholar
• Gildenblat, J. & Klaiman, E. Self-supervised similarity learning for digital pathology. Preprint at https://arxiv.org/abs/1905.08139 (2019).
• Srinidhi, C. L., Kim, S. W., Chen, F.-D. & Martel, A. L. Self-supervised driven consistency training for annotation efficient histopathology image analysis. Med. Image Anal. 75, 102256 (2022).
  Article PubMed Google Scholar
• Schirris, Y. et al. WeakSTIL: weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need. Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology 120390B (4 April 2022).
• Radford, A. et al. Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (eds. Meila, M. & Zhang, T.) 8748–8763 (PMLR, 2021).
• Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K. & Mahmood, F. Synthetic data in machine learning for medicine and healthcare. Nat. Biomed. Eng. 5, 493–497 (2021).
  Article PubMed PubMed Central Google Scholar
• Kather, J. N., Ghaffari Laleh, N., Foersch, S. & Truhn, D. Medical domain knowledge in domain-agnostic generative AI. NPJ Digit. Med. 5, 90 (2022).
• Levine, A. B. et al. Synthesis of diagnostic quality cancer pathology images by generative adversarial networks. J. Pathol. 252, 178–188 (2020).
• Krause, J. et al. Deep learning detects genetic alterations in cancer histology generated by adversarial networks. J. Pathol. 254, 70–79 (2021).
  PubMed Google Scholar
• Xu, Y., Zhu, J.-Y., Chang, E. I.-C., Lai, M. & Tu, Z. Weakly supervised histopathology cancer image segmentation and classification. Med. Image Anal. 18, 591–604 (2014).
  Article PubMed Google Scholar
• Xu, Y., Zhang, J., Chang, E. I.-C., Lai, M. & Tu, Z. Context-constrained multiple instance learning for histopathology image segmentation. Med. Image Comput. Comput. Assist. Interv. 15, 623–630 (2012).
  PubMed Google Scholar
• Couture, H. D., Marron, J. S., Perou, C. M., Troester, M. A. & Niethammer, M. Multiple instance learning for heterogeneous images: training a CNN for histopathology. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science (eds. Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) Vol. 11071 (Springer, 2018).
• Ghaffari Laleh, N. et al. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med. Image Anal. 79, 102474 (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).
• Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017).
• Dosovitskiy, A. et al. An image is worth 16 × 16 words: transformers for image recognition at scale. Preprint at https://arxiv.org/abs/2010.11929 (2020).
• Touvron, H. et al. Training data-efficient image transformers & distillation through attention. Preprint at https://arxiv.org/abs/2012.12877 (2020).
• Paul, S. & Chen, P.-Y. Vision transformers are robust learners. Proc. AAAI Conference on Artificial Intelligence 36, 2 (2022).
• Laleh, N. G. et al. Adversarial attacks and adversarial robustness in computational pathology. Preprint at bioRxiv https://doi.org/10.1101/2022.03.15.484515 (2022).
• Chen, X., Hsieh, C.-J. & Gong, B. When vision transformers outperform ResNets without pre-training or strong data augmentations. Preprint at https://arxiv.org/abs/2106.01548 (2021).
• Lu, M. Y. et al. Federated learning for computational pathology on gigapixel whole slide images. Med. Image Anal. 76, 102298 (2022).
• Warnat-Herresthal, S. et al. Swarm Learning for decentralized and confidential clinical machine learning. Nature 594, 265–270 (2021).
  Article CAS PubMed PubMed Central Google Scholar
• Saldanha, O. L. et al. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat. Med. 28, 1232–1239 (2022).
• DuMont Schütte, A. et al. Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation. NPJ Digit. Med. 4, 141 (2021).
  Article PubMed PubMed Central Google Scholar
• Chen, D., Yu, N., Zhang, Y. & Fritz, M. GAN-Leaks: a taxonomy of membership inference attacks against generative models. Preprint at https://arxiv.org/abs/1909.03935 (2019).
• Schömig-Markiefka, B. et al. Quality control stress test for deep learning-based diagnostic model in digital pathology. Mod. Pathol. 34, 2098–2108 (2021).
  Article PubMed PubMed Central Google Scholar
• Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M. & Madabhushi, A. HistoQC: an open-source quality control tool for digital pathology slides. JCO Clin. Cancer Inform. 3, 1–7 (2019).
  Article PubMed Google Scholar
• Ren, J., Hacihaliloglu, I., Singer, E. A., Foran, D. J. & Qi, X. Unsupervised domain adaptation for classification of histopathology whole-slide images. Front. Bioeng. Biotechnol. 7, 102 (2019).
  Article PubMed PubMed Central Google Scholar
• Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H. & Ferrante, E. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc. Natl Acad. Sci. USA 117, 12592–12594 (2020).
  Article CAS PubMed PubMed Central Google Scholar
• Cirillo, D. et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. NPJ Digit. Med. 3, 81 (2020).
  Article PubMed PubMed Central Google Scholar
• Kundu, S. AI in medicine must be explainable. Nat. Med. 27, 1328 (2021).
  Article CAS PubMed Google Scholar
• Ghassemi, M., Oakden-Rayner, L. & Beam, A. L. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3, e745–e750 (2021).
  Article PubMed Google Scholar
• Foersch, S. et al. Deep learning for diagnosis and survival prediction in soft tissue sarcoma. Ann. Oncol. 32, 1178–1187 (2021).
  Article CAS PubMed Google Scholar
• Carter, S., Armstrong, Z., Schubert, L., Johnson, I. & Olah, C. Activation Atlas. Distill 4.3, e15 (2019); https://distill.pub/2019/activation-atlas/
• Goh, G. et al. Multimodal neurons in artificial neural networks. Distill https://doi.org/10.23915/distill.00030 (2021).
• Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans. Neural Netw. Learn. Syst. 32, 4793–4813 (2021).
  Article PubMed Google Scholar
• Gunning, D. et al. XAI—explainable artificial intelligence. Sci. Robot. 4, eaay7120 (2019).
  Article PubMed Google Scholar
• Zhang, Y., Jiang, H., Miura, Y., Manning, C. D. & Langlotz, C. P. Contrastive learning of medical visual representations from paired images and text. Preprint at https://arxiv.org/abs/2010.00747 (2020).
• Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
  Article CAS PubMed PubMed Central Google Scholar
• Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
  Article PubMed PubMed Central Google Scholar
• van Treeck, M. et al. DeepMed: a unified, modular pipeline for end-to-end deep learning in computational pathology. Preprint at bioRxiv https://doi.org/10.1101/2021.12.19.473344 (2021).
• Pocock, J. et al. TIAToolbox: an end-to-end toolbox for advanced tissue image analytics. Preprint at bioRxiv https://doi.org/10.1101/2021.12.23.474029 (2021).
• Rosenthal, J. et al. Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology. Mol. Cancer Res. 20, 202–206 (2021).
• Dolezal, J., Kochanny, S. & Howard, F. jamesdolezal/slideflow: slideflow 1.0—official public release. Zenodo https://doi.org/10.5281/zenodo.5708490 (2021).
• Cruz Rivera, S. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat. Med. 26, 1351–1363 (2020).
  Article CAS PubMed PubMed Central Google Scholar
• Norgeot, B. et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat. Med. 26, 1320–1324 (2020).
  Article CAS PubMed PubMed Central Google Scholar
• 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 Digit. Health 2, e407–e416 (2020).
  Article PubMed Google Scholar
• Center for Devices & Radiological Health. Good Machine Learning Practice for Medical Device Development https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles (2021).
• Office of the Commissioner. FDA Authorizes Software that Can Help Identify Prostate Cancer https://www.fda.gov/news-events/press-announcements/fda-authorizes-software-can-help-identify-prostate-cancer (2021).
• Kather, J. N. & Calderaro, J. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer. Nat. Rev. Gastroenterol. Hepatol. 17, 591–592 (2020).
  Article PubMed Google Scholar