Summers, Ronald M.

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging

AI in cancer detectionAdvanced Neural Network Applications

Cited by 5,759

The Medical Segmentation Decathlon

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 1,384

Preparing Medical Imaging Data for Machine Learning

Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 1,020

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., & Summers, R. M. (2015) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 784

Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks

Sandfort, V., Yan, K., Pickhardt, P. J., & Summers, R. M. (2019) · Scientific Reports

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 684

Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation

Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 608

Artificial intelligence for the detection of COVID-19 pneumonia on chest ct using multinational datasets

Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. (2020) · Nature Communications

COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

Cited by 587

Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning

Yan, K., Wang, X., Lu, L., & Summers, R. M. (2018) · Journal of medical imaging

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 573

On the interpretability of artificial intelligence in radiology: challenges and opportunities

Reyes, M., Meier, R., Pereira, S., Silva, C. A., Dahlweid, F.-M., Tengg-Kobligk, H. V., Summers, R. M., & Wiest, R. (2020) · Radiology: Artificial Intelligence

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 484

Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation

Roth, H. R., Lu, L., Lay, N., Harrison, A. P., Farag, A., Sohn, A., & Summers, R. M. (2018) · Medical Image Analysis

Medical Image Segmentation TechniquesMedical Imaging and Analysis

Cited by 325

Understanding metric-related pitfalls in image analysis validation

Reinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., et al. (2024) · Nature Methods

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 241

Anatomy-specific classification of medical images using deep convolutional nets

Roth, H. R., Lee, C. T., Shin, H.-C., Seff, A., Kim, L., Yao, J., Lu, L., & Summers, R. M. (2015) · 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

Medical Imaging and AnalysisRadiomics and Machine Learning in Medical Imaging

Cited by 188

NegBio: a high-performance tool for negation and uncertainty detection in radiology reports

Peng, Y., Wang, X., Lu, L., Bagheri, M., Summers, R., & Lu, Z. (2017) · arXiv

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 175

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database

Yan, K., Wang, X., Lu, L., Zhang, L., Harrison, A. P., Bagheri, M., & Summers, R. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 150

Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine

Jin, Q., Chen, F., Zhou, Y., Xu, Z., Cheung, J. M., Chen, R., Summers, R. M., Rousseau, J. F., Ni, P., Landsman, M. J., et al. (2024) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 146

Common Limitations of Image Processing Metrics: A Picture Story

Reinke, A., Tizabi, M. D., Sudre, C. H., Eisenmann, M., Rädsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel, T., Bakas, S., et al. (2021) · arXiv

Medical Image Segmentation TechniquesCell Image Analysis Techniques

Cited by 126

Interleaved text/image Deep Mining on a large-scale radiology database

Shin, H.-C., Lu, L., Kim, L., Seff, A., Yao, J., & Summers, R. M. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 94

Metrics reloaded: Recommendations for image analysis validation

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., et al. (2022) · arXiv

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 59

Segmentation label propagation using deep convolutional neural networks and dense conditional random field

Gao, M., Xu, Z., Lu, L., Wu, A., Nogues, I., Summers, R. M., & Mollura, D. J. (2016) · 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 48

DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations

Yan, K., Wang, X., Lu, L., & Summers, R. M. (2017) · arXiv

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 40

Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports

Zhu, Q., Mathai, T. S., Mukherjee, P., Peng, Y., Summers, R. M., & Lu, Z. (2023) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 36

Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection

Seff, A., Lu, L., Barbu, A., Roth, H., Shin, H.-C., & Summers, R. M. (2015) · Lecture Notes in Computer Science

AI in cancer detectionMedical Image Segmentation Techniques

Cited by 36

Intrapatient Changes in CT-Based Body Composition After Initiation of Semaglutide (Glucagon-Like Peptide-1 Receptor Agonist) Therapy

Nelson, L. W., Lee, M. H., Garrett, J. W., Pickhardt, S. G., Warner, J. D., Summers, R. M., & Pickhardt, P. J. (2024) · American Journal of Roentgenology

Diabetes Treatment and ManagementBody Composition Measurement Techniques

Cited by 35

Confounding factors need to be accounted for in assessing bias by machine learning algorithms

Mukherjee, P., Shen, T. C., Liu, J., Mathai, T., Shafaat, O., & Summers, R. M. (2022) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 26