41551 2024 1223 Fig1 HTML

A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies

Posted by


  • Kirillov, A. et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 4015–4026 (IEEE, 2023).

  • Gamper, J., Alemi Koohbanani, N., Benet, K., Khuram, A. & Rajpoot, N. PanNuke: An open pan-cancer histology dataset for nuclei instance segmentation and classification. In Digital Pathology. ECDP 2019. Lecture Notes in Computer Science Vol. 11435 (eds Reyes-Aldasoro, C. C. et al.) 11–19 (Springer, 2019).

  • 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
     

  • Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical Twitter. Nat. Med. 29, 2307–2316 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lu, M. Y. et al. A visual-language foundation model for computational pathology. Nat. Med. 30, 863–874 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chen, R. J. et al. Towards a general-purpose foundation model for computational pathology. Nat. Med. 30, 850–862 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Amgad, M. et al. A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer. Nat. Med. 30, 85–97 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jiang, X. et al. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study. Lancet Digit. Health 6, e33–e43 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liu, Y. et al. Artificial intelligence-based breast cancer nodal metastasis detection: insights into the black box for pathologists. Arch. Pathol. Lab. Med. 143, 859–868 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Krogue, J. D. et al. Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning. Commun. Med. 3, 59 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang, Z. et al. Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images. npj Precis. Oncol. 7, 14 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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
     

  • He, J. et al. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 25, 30–36 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Price, W. N. II, Gerke, S. & Cohen, I. G. Potential liability for physicians using artificial intelligence. JAMA 322, 1765–1766 (2019).

  • Acs, B., Rantalainen, M. & Hartman, J. Artificial intelligence as the next step towards precision pathology. J. Intern. Med. 288, 62–81 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kiani, A. et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. npj Digit. Med. 3, 23 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Challa, B. et al. Artificial intelligence-aided diagnosis of breast cancer lymph node metastasis on histologic slides in a digital workflow. Mod. Pathol. 36, 100216 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Bankhead, P., Loughrey, M. B. & Fernández, J. A. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chiu, C. & Clack, N. Napari: a Python multi-dimensional image viewer platform for the research community. Microsc. Microanal. 28(S1), 1576–1577 (2022).

    Article 

    Google Scholar
     

  • Aubreville, M., Bertram, C., Klopfleisch, R. & Maier, A. SlideRunner—a tool for massive cell annotations in whole slide images. in Bildverarbeitung für die Medizin 2018 (eds Maier, A. et al.) 309–314 (Springer, 2018).

  • Pocock, J. et al. TIAToolbox as an end-to-end library for advanced tissue image analytics. Commun. Med. 2, 120 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • MONAI model zoo. GitHub https://github.com/Project-MONAI/model-zoo (2022).

  • Amgad, M. et al. HistomicsTK. GitHub https://digitalslidearchive.github.io/HistomicsTK/ (2016).

  • Dietvorst, B. J., Simmons, J. P. & Massey, C. Overcoming algorithm aversion: people will use imperfect algorithms if they can (even slightly) modify them. Manage. Sci. 64, 1155–1170 (2018).

    Article 

    Google Scholar
     

  • Longoni, C., Bonezzi, A. & Morewedge, C. K. Resistance to medical artificial intelligence. J. Consum. Res. 46, 629–650 (2019).

    Article 

    Google Scholar
     

  • Medela, A. et al. Few shot learning in histopathological images: reducing the need of labeled data on biological datasets. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) 1860–1864 (IEEE, 2019).

  • van Rijthoven, M. et al. Few-shot weakly supervised detection and retrieval in histopathology whole-slide images. In Medical Imaging 2021: Digital Pathology Vol. 11603, 137–143 (SPIE, 2021).

  • Chen, J., Jiao, J., He, S., Han, G. & Qin, J. Few-shot breast cancer metastases classification via unsupervised cell ranking. IEEE/ACM Trans. Comput. Biol. Bioinform. 18, 1914–1923 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Zhu, Z. et al. EasierPath: an open-source tool for human-in-the-loop deep learning of renal pathology. In Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC 2020, MIL3ID 2020, LABELS 2020 Vol. 12446 (eds Cardoso, J., et al.) 214–222 (Springer, 2020).

  • Singh, H. & Graber, M. L. Improving diagnosis in health care–the next imperative for patient safety. N. Engl. J. Med. 373, 2493–2495 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Erickson, L. A., Mete, O., Juhlin, C. C., Perren, A. & Gill, A. J. Overview of the 2022 WHO classification of parathyroid tumors. Endocr. Pathol. 33, 64–89 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Budd, S., Robinson, E. C. & Kainz, B. A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med. Image Anal. 71, 102062 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • van der Wal, D. et al. Biological data annotation via a human-augmenting AI-based labeling system. npj Digit. Med. 4, 145 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Settles, B. Active Learning Literature Survey (University of Wisconsin-Madison Department of Computer Sciences, 2009); https://digital.library.wisc.edu/1793/60660

  • Go, H. Digital pathology and artificial intelligence applications in pathology. Brain Tumor Res. Treat. 10, 76–82 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wen, S. et al. Comparison of different classifiers with active learning to support quality control in nucleus segmentation in pathology images. AMIA Jt. Summits Transl. Sci. Proc. 2017, 227–236 (2018).

    PubMed 

    Google Scholar
     

  • Hamilton, P. W. et al. Digital pathology and image analysis in tissue biomarker research. Methods 70, 59–73 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cheng, J. et al. Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis. Cancer Res. 77, e91–e100 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McQueen, D. B., Perfetto, C. O., Hazard, F. K. & Lathi, R. B. Pregnancy outcomes in women with chronic endometritis and recurrent pregnancy loss. Fertil. Steril. 104, 927–931 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Ryan, E. et al. The menstrual cycle phase impacts the detection of plasma cells and the diagnosis of chronic endometritis in endometrial biopsy specimens. Fertil. Steril. 118, 787–794 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kim, H. J. & Choi, G.-S. Clinical Implications of lymph node metastasis in colorectal cancer: current status and future perspectives. Ann. Coloproctol. 35, 109–117 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kiehl, L. et al. Deep learning can predict lymph node status directly from histology in colorectal cancer. Eur. J. Cancer 157, 464–473 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Khan, A. et al. Computer-assisted diagnosis of lymph node metastases in colorectal cancers using transfer learning with an ensemble model. Mod. Pathol. 36, 100118 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Mescoli, C. et al. Isolated tumor cells in regional lymph nodes as relapse predictors in stage I and II colorectal cancer. J. Clin. Oncol. 30, 965–971 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Tizhoosh, H. R. & Pantanowitz, L. Artificial intelligence and digital pathology: challenges and opportunities. J. Pathol. Inform. 9, 38 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baxi, V. et al. Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab. Mod. Pathol. 35, 1529–1539 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Graham, S. et al. Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study. Gut 72, 1709–1721 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Alemi Koohbanani, N., Jahanifar, M., Zamani Tajadin, N. & Rajpoot, N. NuClick: a deep learning framework for interactive segmentation of microscopic images. Med. Image Anal. 65, 101771 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Schemmer, M., Kühl, N., Benz, C. & Satzger, G. On the influence of explainable AI on automation bias. Preprint at https://arxiv.org/abs/2204.08859 (2022).

  • Bond, R. R. et al. Automation bias in medicine: the influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J. Electrocardiol. 51, S6–S11 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Parikh, R. B., Teeple, S. & Navathe, A. S. Addressing bias in artificial intelligence in health care. JAMA 322, 2377–2378 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Alon-Barkat, S. & Busuioc, M. Human–AI interactions in public sector decision making: ‘automation bias’ and ‘selective adherence’ to algorithmic advice. J. Public Adm. Res. Theory 33, 153–169 (2022).

    Article 

    Google Scholar
     

  • Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell detection with star-convex polygons. In Medical Image Computing and Computer Assisted InterventionMICCAI 2018. Lecture Notes in Computer Science Vol. 11071 (eds Frangi, A. et al.) 265–273 (Springer, 2018).

  • Haralick, R. M., Shanmugam, K. & Dinstein, H. I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973).

    Article 

    Google Scholar
     

  • Liu, Z. et al. A ConvNet for the 2020s. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 11966–11976 (IEEE, 2022).

  • Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).

  • Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J. & Fernández-Leal, Á. Human-in-the-loop machine learning: a state of the art. Artif. Intell. Rev. 56, 3005–3054 (2023).

    Article 

    Google Scholar
     

  • He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, 2016).

  • Deng, J. et al. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).

  • Li, W., Zhu, X. & Gong, S. Harmonious attention network for person re-identification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2285–2294 (IEEE, 2018).

  • McHugh, M. L. Interrater reliability: the kappa statistic. Biochem. Med. 22, 276–282 (2012).

    Article 

    Google Scholar
     

  • Zou, K. H., Fielding, J. R., Silverman, S. G. & Tempany, C. M. C. Hypothesis testing I: proportions. Radiology 226, 609–613 (2003).

    Article 
    PubMed 

    Google Scholar
     



  • Source link

    Leave a Reply

    Your email address will not be published. Required fields are marked *