Oktay, Ozan

Attention U-Net: Learning Where to Look for the Pancreas

Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 5,458

Attention gated networks: learning to leverage salient regions in medical images

Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 2,014

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A. M., Aung, N., Lukaschuk, E., Sanghvi, M. M., et al. (2018) · Journal of Cardiovascular Magnetic Resonance

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 773

Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation

Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 751

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M. A., Hajnal, J. V., Kainz, B., et al. (2017) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 410

Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

Boecking, B., Usuyama, N., Bannur, S., Castro, D. C., Schwaighofer, A., Hyland, S., Wetscherek, M., Naumann, T., Nori, A., Alvarez-Valle, J., et al. (2022) · Lecture Notes in Computer Science

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 317

Multi-input Cardiac Image Super-Resolution Using Convolutional Neural Networks

Oktay, O., Bai, W., Lee, M., Guerrero, R., Kamnitsas, K., Caballero, J., de Marvao, A., Cook, S., O’Regan, D., & Rueckert, D. (2016) · Lecture Notes in Computer Science

Advanced Image Processing TechniquesMedical Image Segmentation Techniques

Cited by 246

Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

Bannur, S., Hyland, S., Liu, Q., Pérez-García, F., Ilse, M., Castro, D. C., Boecking, B., Sharma, H., Bouzid, K., Thieme, A., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 227

Exploring scalable medical image encoders beyond text supervision

Pérez-García, F., Sharma, H., Bond-Taylor, S., Bouzid, K., Salvatelli, V., Ilse, M., Bannur, S., Castro, D. C., Schwaighofer, A., Lungren, M. P., et al. (2025) · Nature Machine Intelligence

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 115

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

Bernard, O., Bosch, J. G., Heyde, B., Alessandrini, M., Barbosa, D., Camarasu-Pop, S., Cervenansky, F., Valette, S., Mirea, O., Bernier, M., et al. (2016) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesCardiovascular Function and Risk Factors

Cited by 110

Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology

Yildirim, N., Richardson, H., Wetscherek, M. T., Bajwa, J., Jacob, J., Pinnock, M. A., Harris, S., Coelho De Castro, D., Bannur, S., Hyland, S., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 82

Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging

Meng, Q., Housden, J., Matthew, J., Rueckert, D., Schnabel, J. A., Kainz, B., Sinclair, M., Zimmer, V., Hou, B., Rajchl, M., et al. (2019) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 52

MAIRA-2: Grounded Radiology Report Generation

Bannur, S., Bouzid, K., Castro, D. C., Schwaighofer, A., Thieme, A., Bond-Taylor, S., Ilse, M., Pérez-García, F., Salvatelli, V., Sharma, H., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 19

Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology

Thieme, A., Rajamohan, A., Cooper, B., Groombridge, H., Simister, R., Wong, B., Woznitza, N., Pinnock, M. A., Wetscherek, M. T., Morrison, C., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 10