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A completely open AI basis mannequin utilized to chest radiography

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  • Broder, J. S. Diagnostic Imaging for the Emergency Doctor (ed. Broder, J. S.) Ch. 5, 185–296 (W. B. Saunders, 2011).

  • Çallı, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, Ok. G. & Murphy, Ok. Deep studying for chest X-ray evaluation: a survey. Med. Picture Anal. 72, 102125 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Tajbakhsh, N., Roth, H., Terzopoulos, D. & Liang, J. Visitor editorial annotation-efficient deep studying: the holy grail of medical imaging. IEEE Trans. Med. Imaging 40, 2526–2533 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. & Aerts, H. J. Synthetic intelligence in radiology. Nat. Rev. Most cancers 18, 500–510 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhou, Y. et al. A basis mannequin for generalizable illness detection from retinal photographs. Nature 622, 156–163 (2023).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visible–language basis mannequin for pathology picture evaluation utilizing medical twitter. Nat. Med. 29, 2307–2316 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Christensen, M., Vukadinovic, M., Yuan, N. & Ouyang, D. Imaginative and prescient–language basis mannequin for echocardiogram interpretation. Nat. Med. 30, 1481–1488 (2024).

  • Tiu, E. et al. Knowledgeable-level detection of pathologies from unannotated chest X-ray photographs through self-supervised studying. Nat. Biomed. Eng. 6, 1399–1406 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Information-enhanced visual-language pre-training on chest radiology photographs. Nat. Commun. 14, 4542 (2023).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sellergren, A. B. et al. Simplified switch studying for chest radiography fashions utilizing much less knowledge. Radiology 305, 454–465 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Azizi, S. et al. Strong and data-efficient generalization of self-supervised machine studying for diagnostic imaging. Nat. Biomed. Eng. 7, 756–779 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Xu, S. et al. ELIXR: in the direction of a normal objective X-ray synthetic intelligence system by means of alignment of enormous language fashions and radiology imaginative and prescient encoders. Preprint at arxiv.org/abs/2308.01317 (2023).

  • Basdevant, A. et al. In direction of a framework for openness in basis fashions: proceedings from the Columbia Convening on openness in synthetic intelligence. Preprint at arxiv.org/abs/2405.15802 (2024).

  • Ma, D., Pang, J., Gotway, M. B. & Liang, J. Basis Ark: accruing and reusing data for superior and strong efficiency. In Proc. Worldwide Convention on Medical Picture Computing and Laptop-Assisted Intervention (eds Greenspan, H. et al.) 651–662 (Springer, 2023).

  • Liu, Z. et al. Swin transformer: hierarchical imaginative and prescient transformer utilizing shifted home windows. In Proc. IEEE/CVF Worldwide Convention on Laptop Imaginative and prescient (eds Hassner, T. et al.) 10012–10022 (IEEE, 2021).

  • Velan, S. S. Benchmarking and Boosting Localizers for Chest X-rays. Grasp’s thesis, Arizona State Univ. (2024).

  • Saravanan, M. Benchmarking and Boosting of 3D Segmentation Fashions. Grasp’s thesis, Arizona State Univ. (2024).

  • Islam, N. U. et al. Basis X: integrating classification, localization, and segmentation by means of lock-release pretraining technique for chest X-ray evaluation. In Proc. IEEE/CVF Winter Convention on Purposes of Laptop Imaginative and prescient (eds Biswas, S. et al.) 3647–3656 (IEEE, 2025).

  • Wang, X. et al. Chestx-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of frequent thorax illnesses. In Proc. IEEE Convention on Laptop Imaginative and prescient and Sample Recognition (eds Cucchiara, R. et al.) 2097–2106 (IEEE, 2017).

  • Pérez-García, F. et al. Exploring scalable medical picture encoders past textual content supervision. Nat. Mach. Intell. 7, 119–130 (2025).

  • Ma, D. et al. Benchmarking and boosting transformers for medical picture classification. In Proc. MICCAI Workshop on Area Adaptation and Illustration Switch (eds Kamnitsas, Ok. et al.) 12–22 (Springer, 2022).

  • Cho, Ok. et al. Chess: chest X-ray pre-trained mannequin through self-supervised contrastive studying. J. Digit. Imaging 36, 902–910 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kang, M. et al. Label-assemble: leveraging a number of datasets with partial labels. In Proc. twentieth Worldwide Symposium on Biomedical Imaging (eds Salvado, O. et al.) 1–5 (IEEE, 2023).

  • Lee, J. et al. Deep studying for uncommon illness: a scoping evaluation. J. Biomed. Inform. 135, 104227 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Yaqing, W., Quanming, Y., Kwok James, T. & Ni Lionel, M. Generalizing from a couple of examples: a survey on few-shot studying. ACM Comput. Surv. 53, 1–34 (2020).


    Google Scholar
     

  • Holste, G. et al. CXR-LT: multi-label long-tailed classification on chest X-rays. PhysioNet 5, 19 (2023).


    Google Scholar
     

  • Zhou, S. Ok. et al. A evaluation of deep studying in medical imaging: imaging traits, expertise tendencies, case research with progress highlights, and future guarantees. Proc. IEEE 109, 820–838 (2021).

    Article 

    Google Scholar
     

  • Wang, D. et al. An actual-world dataset and benchmark for basis mannequin adaptation in medical picture classification. Sci. Knowledge 10, 574 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, L. et al. Generalizing deep studying for medical picture segmentation to unseen domains through deep stacked transformation. IEEE Trans. Med. Imaging 39, 2531–2540 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cohen, J. P. et al. TorchXRayVision: a library of chest X-ray datasets and fashions. In Proc. Worldwide Convention on Medical Imaging with Deep Studying (eds Konukoglu, E. et al.) 231–249 (PMLR, 2022).

  • Glocker, B., Jones, C., Roschewitz, M. & Winzeck, S. Danger of bias in chest radiography deep studying basis fashions. Radiol.: Artif. Intell. 5, e230060 (2023).

    PubMed 

    Google Scholar
     

  • Seyyed-Kalantari, L., Zhang, H., McDermott, M. B., Chen, I. Y. & Ghassemi, M. Underdiagnosis bias of synthetic intelligence algorithms utilized to chest radiographs in under-served affected person populations. Nat. Med. 27, 2176–2182 (2021).

    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 analysis. Proc. Natl Acad. Sci. USA 117, 12592–12594 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Irvin, J. et al. CheXpert: a big chest radiograph dataset with uncertainty labels and knowledgeable comparability. In Proc. AAAI Convention on Synthetic Intelligence, Vol. 33 (eds Hentenryck, P. V. & Zhou, Z. H.) 590–597 (AAAI, 2019).

  • Wang, L., Lin, Z. Q. & Wong, A. Covid-net: a tailor-made deep convolutional neural community design for detection of COVID-19 instances from chest X-ray photographs. Sci. Rep. 10, 19549 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, F. et al. A medical multimodal giant language mannequin for future pandemics. npj Digit. Med. 6, 226 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xiao, J., Bai, Y., Yuille, A. & Zhou, Z. Delving into masked autoencoders for multi-label thorax illness classification. In Proc. IEEE/CVF Winter Convention on Purposes of Laptop Imaginative and prescient (eds Crandall, D. et al.) 3588–3600 (IEEE, 2023).

  • Van der Maaten, L. & Hinton, G. Visualizing knowledge utilizing t-SNE. J. Mach. Study. Res. 9, 2579–2605 (2008).

    MATH 

    Google Scholar
     

  • Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nat. Med. 28, 1773–1784 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Soenksen, L. R. et al. Built-in multimodal synthetic intelligence framework for healthcare purposes. npj Digit. Med. 5, 149 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ye, M., Fang, X., Du, B., Yuen, P. C. & Tao, D. Heterogeneous federated studying: state-of-the-art and analysis challenges. ACM Comput. Surv. 56, 1–44 (2023).


    Google Scholar
     

  • Nguyen, H. Q. et al. VinDr-CXR: an open dataset of chest X-rays with radiologist’s annotations. Sci. Knowledge 9, 429 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Anouk Stein, M. et al. RSNA Pneumonia Detection Problem. Kaggle https://kaggle.com/competitions/rsna-pneumonia-detection-challenge (2018).

  • Jaeger, S. et al. Two public chest X-ray datasets for computer-aided screening of pulmonary illnesses. Quant. Imaging Med. Surg. 4, 475 (2014).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Johnson, A. E. et al. MIMIC-CXR, a de-identified publicly obtainable database of chest radiographs with free-text studies. Sci. Knowledge 6, 317 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tajbakhsh, N. et al. Convolutional neural networks for medical picture evaluation: full coaching or superb tuning? IEEE Trans. Med. Imaging 35, 1299–1312 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Zawacki, A. et al. SIIM-ACR pneumothorax segmentation. Kaggle https://kaggle.com/competitions/siim-acr-pneumothorax-segmentation (2019).

  • Sogancioglu, E. et al. Nodule detection and era on chest X-rays: NODE21 problem. IEEE Trans. Med. Imaging 43, 2839–2853 (2024).

  • Goldbaum, M., Kermany, D. & Zhang, Ok. Labeled optical coherence tomography (OCT) and chest X-ray photographs for classification. Mendeley Knowledge https://doi.org/10.17632/rscbjbr9sj.2 (2018).

  • Liu, Y., Wu, Y.-H., Ban, Y., Wang, H. & Cheng, M.-M. Rethinking computer-aided tuberculosis analysis. In Proc. IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition (eds Liu, C. et al.) 2646–2655 (IEEE, 2020).

  • Khosla, P. et al. Supervised contrastive studying. In Proc. thirty third Advances in Neural Data Processing Programs (eds Larochelle, H. et al.) 18661–18673 (Curran Associates, 2020).

  • Oquab, M. et al. DINOv2: studying strong visible options with out supervision. Transact. Mach. Study. Res. https://openreview.internet/discussion board?id=a68SUt6zFt (2024).

  • Xie, Z. et al. SimMIM: a easy framework for masked picture modeling. In Proc. IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition (eds Dana, Ok. et al.) 9653–9663 (IEEE, 2022).

  • Chen, X., Fan, H., Girshick, R. & He, Ok. Improved baselines with momentum contrastive studying. Preprint at arxiv.org/abs/2003.04297 (2020).

  • Cohen, J. P., Hashir, M., Brooks, R. & Bertrand, H. On the boundaries of cross-domain generalization in automated X-ray prediction. In Proc. Medical Imaging with Deep Studying (eds Arbel, T. et al.) 136–155 (PMLR, 2020).

  • Unal, I. Defining an optimum cut-point worth in roc evaluation: another strategy. Comput. Math. Strategies Med. 2017, 3762651 (2017).

    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar
     

  • Jennewein, D. M. et al. The Sol supercomputer at Arizona State College. In Proc. Observe and Expertise in Superior Analysis Computing (eds Sinkovits, R. & Romanella, A.) 296–301 (ACM, 2023).

  • Track, C., Granqvist, F. & Talwar, Ok. Aptitude: federated studying annotated picture repository. In Proc. thirty fifth Advances in Neural Data Processing Programs (eds Koyejo, S. et al.) 37792–37805 (Curran Associates, 2022).

  • Yan, R. et al. Label-efficient self-supervised federated studying for tackling knowledge heterogeneity in medical imaging. IEEE Trans. Med. Imaging 42, 1932–1943 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

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