A generative model uses healthy and diseased image pairs for pixel-level chest X-ray pathology localization

Wait 5 sec.

Çallı, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K. G. & Murphy, K. Deep learning for chest X-ray analysis: a survey. Med. Image Anal. 72, 102125 (2021).Article  PubMed  Google Scholar Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nat. Commun. 14, 4542 (2023).Article  CAS  PubMed  PubMed Central  Google Scholar Tiu, E. et al. Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning. Nat. Biomed. Eng. 6, 1399–1406 (2022).Article  PubMed  PubMed Central  Google Scholar Yang, S., Wu, X., Ge, S., Zhou, S. K. & Xiao, L. Knowledge matters: chest radiology report generation with general and specific knowledge. Med. Image Anal. 80, 102510 (2022).Article  PubMed  Google Scholar Zhou, H.-Y. et al. Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports. Nat. Mach. Intell. 4, 32–40 (2022).Article  Google Scholar Liu, W. et al. Automatic lung segmentation in chest X-ray images using improved U-Net. Sci. Rep. 12, 8649 (2022).Article  CAS  PubMed  PubMed Central  Google Scholar Tang, Y.-B., Tang, Y.-X., Xiao, J. & Summers, R. M. Xlsor: a robust and accurate lung segmentor on chest X-rays using criss-cross attention and customized radiorealistic abnormalities generation. In International Conference on Medical Imaging with Deep Learning 457–467 (PMLR, 2019).Ieki, H. et al. Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis. Commun. Med. 2, 159 (2022).Article  PubMed  PubMed Central  Google Scholar Agu, N. N. et al. AnaXNet: anatomy aware multi-label finding classification in chest X-ray. In Medical Image Computing and Computer Assisted Intervention (eds Greenspan, H. et al.) 804–813 (Springer, 2021).Wang, X., Peng, Y., Lu, L., Lu, Z. & Summers, R. M. Tienet: text-image embedding network for common thorax disease classification and reporting in chest X-rays. In IEEE Conference on Computer Vision and Pattern Recognition 9049–9058 (IEEE, 2018).Xiang, T. et al. SQUID: deep feature in-painting for unsupervised anomaly detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition 23890–23901 (IEEE, 2023).Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I. Y. & Ghassemi, M. Chexclusion: fairness gaps in deep chest X-ray classifiers. In Biocomputing 2021: Proc. Pacific Symposium 232–243 (World Scientific, 2020).Oussidi, A. & Elhassouny, A. Deep generative models: survey. In International Conference on Intelligent Systems and Computer Vision 1–8 (IEEE, 2018).Cao, Y. et al. A comprehensive survey of ai-generated content (AIGC): a history of generative AI from GAN to ChatGPT. Preprint at https://arxiv.org/abs/2303.04226 (2023).Creswell, A. et al. Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35, 53–65 (2018).Article  Google Scholar Ramesh, A. et al. Zero-shot text-to-image generation. In International Conference on Machine Learning 8821–8831 (PMLR, 2021).Goodfellow, I. et al. Generative adversarial networks. Commun. ACM 63, 139–144 (2020).Article  Google Scholar Song, Y. & Ermon, S. Generative modeling by estimating gradients of the data distribution. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) https://openreview.net/pdf?id=B1lcYrBgLH (NeurIPS, 2019).Tschuchnig, M. E. & Gadermayr, M. Anomaly detection in medical imaging – a mini review. In International Data Science Conference (eds Haber, P. et al.) 33–38 (Springer, 2022).Shvetsova, N., Bakker, B., Fedulova, I., Schulz, H. & Dylov, D. V. Anomaly detection in medical imaging with deep perceptual autoencoders. IEEE Access 9, 118571–118583 (2021).Article  Google Scholar Zhao, H. et al. Anomaly detection for medical images using self-supervised and translation-consistent features. IEEE Trans. Med. Imaging 40, 3641–3651 (2021).Article  PubMed  Google Scholar Le, K. H. et al. Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis. IEEE Access 11, 14105–14114 (2023).Article  Google Scholar Kebaili, A., Lapuyade-Lahorgue, J. & Ruan, S. Deep learning approaches for data augmentation in medical imaging: a review. J. Imaging 9, 81 (2023).Article  PubMed  PubMed Central  Google Scholar Xing, Y. et al. Adversarial pulmonary pathology translation for pairwise chest X-ray data augmentation. In Medical Image Computing and Computer Assisted Intervention (eds Shen, D. et al.) 757–765 (Springer, 2019).Waheed, A. et al. CovidGAN: data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access 8, 91916–91923 (2020).Article  Google Scholar Motamed, S., Rogalla, P. & Khalvati, F. Data augmentation using generative adversarial networks (GANs) for GAN-based detection of pneumonia and COVID-19 in chest X-ray images. Inform. Med. Unlocked 27, 100779 (2021).Article  PubMed  PubMed Central  Google Scholar Kazerouni, A. et al. Diffusion models in medical imaging: a comprehensive survey. Med. Image Anal. 88, 102846 (2023).Johnson, A. E. et al. MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016).Article  CAS  PubMed  PubMed Central  Google Scholar Johnson, A., Pollard, T. & Mark, R. MIMIC-III clinical database (version 1.4). PhysioNet https://doi.org/10.13026/C2XW26 (2016).Chambon, P., Bluethgen, C., Langlotz, C. P. & Chaudhari, A. Adapting pretrained vision-language foundational models to medical imaging domains. Preprint at https://arxiv.org/abs/2210.04133 (2022).Bluethgen, C. et al. A vision–language foundation model for the generation of realistic chest X-ray images. Nat. Biomed. Eng. 9, 494–506 (2025).Zhang, S. & Metaxas, D. On the challenges and perspectives of foundation models for medical image analysis. Med. Image Anal. 91, 102996 (2024).Wu, W., Zhao, Y., Shou, M. Z., Zhou, H. & Shen, C. Diffumask: synthesizing images with pixel-level annotations for semantic segmentation using diffusion models. In IEEE/CVF International Conference on Computer Vision 1206–1217 (IEEE, 2023).Shao, S. et al. DiffuseExpand: expanding dataset for 2D medical image segmentation using diffusion models. Preprint at https://arxiv.org/abs/2304.13416 (2023).Khader, F. et al. Denoising diffusion probabilistic models for 3D medical image generation. Sci. Rep. 13, 7303 (2023).Article  CAS  PubMed  PubMed Central  Google Scholar Yoon, J. S., Zhang, C., Suk, H.-I., Guo, J. & Li, X. SADM: sequence-aware diffusion model for longitudinal medical image generation. In International Conference on Information Processing in Medical Imaging (eds Frangi, A. et al.) 388–400 (Springer, 2023).Du, S. et al. Boosting dermatoscopic lesion segmentation via diffusion models with visual and textual prompts. In IEEE International Symposium on Biomedical Imaging 1–5 (IEEE, 2024).Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition 10684–10695 (IEEE, 2022).Zhang, L., Rao, A. & Agrawala, M. Adding conditional control to text-to-image diffusion models. In IEEE/CVF International Conference on Computer Vision 3836–3847 (IEEE, 2023).Liu, C. & Liu, D. LaCon: late-constraint diffusion guidance for steerable guided image synthesis. Preprint at https://arxiv.org/abs/2305.11520 (2023).Lyu, Q. & Wang, G. Conversion between CT and MRI images using diffusion and score-matching models. Preprint at https://arxiv.org/abs/2209.12104 (2022).Özbey, M. et al. Unsupervised medical image translation with adversarial diffusion models. IEEE Trans. Med. Imaging 42, 3524–3539 (2023).Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. In 34th Conference on Neural Information Processing Systems (NeurIPS 2020) https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf (NeurIPS, 2020).Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D. & Pfeiffer, D. Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization. Sci. Rep. 9, 6268 (2019).Article  CAS  PubMed  PubMed Central  Google Scholar Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. Preprint at https://arxiv.org/abs/1312.6034 (2013).Aggarwal, M. et al. Towards trainable saliency maps in medical imaging. Preprint at https://doi.org/10.48550/arXiv.2011.07482 (2020).Tjoa, E. & Cuntai, G. Quantifying explainability of saliency methods in deep neural networks with a synthetic dataset. IEEE Trans. Artif. Intell. 4, 858–870 (2023).Irvin, J. et al. Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In AAAI Conference on Artificial Intelligence 590–597 (AAAI, 2019).Saporta, A. et al. Benchmarking saliency methods for chest X-ray interpretation. Nat. Mach. Intell. 4, 867–878 (2022).Article  Google Scholar Wang, X. et al. NIH Chest X-ray Dataset of 14 Common Thorax Disease Categories (NIH Clinical Center, 2019).Nguyen, H. C., Le, T. T., Pham, H. H. & Nguyen, H. Q. VinDr-RibCXR: a benchmark dataset for automatic segmentation and labeling of individual ribs on chest X-rays. Preprint at https://arxiv.org/abs/2107.01327 (2021).Chen, J. et al. Transunet: transformers make strong encoders for medical image segmentation. Preprint at https://doi.org/10.48550/arXiv.2102.04306 (2021).Huang, G., Liu, Z., van der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition 4700–4708 (IEEE, 2017).Shih, G. et al. Augmenting the National Institutes Of Health chest radiograph dataset with expert annotations of possible pneumonia. Radiol. Artif. Intell. 1, e180041 (2019).Article  PubMed  PubMed Central  Google Scholar Boecking, B. et al. Making the most of text semantics to improve biomedical vision–language processing. In European Conference on Computer Vision (eds Avidan, S. et al.) 1–21 (Springer, 2022).Liu, J., Lian, J. & Yu, Y. Chestx-Det10: chest X-ray dataset on detection of thoracic abnormalities. Preprint at https://arxiv.org/abs/2006.10550 (2021).Huang, S.-C., Shen, L., Lungren, M. P. & Yeung, S. Gloria: a multimodal global-local representation learning framework for label-efficient medical image recognition. In IEEE/CVF International Conference on Computer Vision 3942–3951 (IEEE, 2021).Wu, C., Zhang, X., Zhang, Y., Wang, Y. & Xie, W. Medklip: medical knowledge enhanced language-image pre-training for X-ray diagnosis. In IEEE/CVF International Conference on Computer Vision 21372–21383 (IEEE, 2023).Yang, H. et al. Generalizable vision-language pre-training for annotation-free pathology localization. Preprint at https://arxiv.org/html/2401.02044v1 (2024).Selvaraju, R. R. et al. Grad-Cam: visual explanations from deep networks via gradient-based localization. In IEEE International Conference on Computer Vision 618–626 (IEEE, 2017).Jiang, P.-T., Zhang, C.-B., Hou, Q., Cheng, M.-M. & Wei, Y. Layercam: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875–5888 (2021).Article  PubMed  Google Scholar Bustos, A., Pertusa, A., Salinas, J.-M. & de la Iglesia-Vaya, M. Padchest: a large chest X-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020).Article  PubMed  Google Scholar Radford, A. et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning 8748–8763 (PMLR, 2021).Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https://arxiv.org/abs/1312.6114 (2013).Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (eds Navab, N. et al.) 234–241 (Springer, 2015).Dong, K. A generative model uses healthy and diseased image pairs for pixel-level chest X-ray pathology localization. Source code. GitHub https://github.com/kaimingd/PIXEL (2025).Download references