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

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

The Pascal Visual Object Classes Challenge: A Retrospective

Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works

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 · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616 · see citing works

Interactive medical image segmentation using deep learning with image-specific fine tuning

Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791721 · Cited by 843 · see citing works

BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment

Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., Zwicker, J. G., & Hamarneh, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.046 · Cited by 813 · see citing works

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Moeskops, P., Viergever, M. A., Mendrik, A. M., de Vries, L. S., Benders, M. J. N. L., & Isgum, I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2548501 · Cited by 861 · see citing works

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679 · see citing works

Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

Christ, P. F., Elshaer, M. E. A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., Rempfler, M., Armbruster, M., Hofmann, F., D’Anastasi, M., et al. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_48 · Cited by 625 · see citing works

Automatic multiorgan segmentation in thorax <scp>CT</scp> images using U‐net‐ <scp>GAN</scp>

Dong, X., Lei, Y., Wang, T., Thomas, M., Tang, L., Curran, W. J., Liu, T., & Yang, X. (2019) · Medical Physics · doi:10.1002/mp.13458 · Cited by 275 · see citing works

Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation

Liu, K.-L., Wu, T., Chen, P.-T., Tsai, Y. M., Roth, H., Wu, M.-S., Liao, W.-C., & Wang, W. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30078-9 · Cited by 222 · see citing works

3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

Xia, Y., Liu, F., Yang, D., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A., & Roth, H. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093608 · Cited by 170 · see citing works

Federated learning for healthcare applications

Chaddad, A., Wu, Y., & Desrosiers, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3325822 · Cited by 136 · see citing works

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors

Si, K., Xue, Y., Yu, X., Zhu, X., Li, Q., Gong, W., Liang, T., & Duan, S. (2021) · Theranostics · doi:10.7150/thno.52508 · Cited by 120 · see citing works

A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images

Wang, Y., Qiu, Y., Thai, T., Moore, K., Liu, H., & Zheng, B. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.03.017 · Cited by 110 · see citing works

Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography

Ho, T. K., & Gwak, J. (2019) · Applied Sciences · doi:10.3390/app9194130 · Cited by 96 · see citing works

A review of deep learning-based deformable medical image registration

Zou, J., Gao, B., Song, Y., & Qin, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1047215 · Cited by 91 · see citing works

Two‐stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra‐reader and inter‐reader reliability at full and reduced radiation dose on an external dataset

Panda, A., Korfiatis, P., Suman, G., Garg, S. K., Polley, E. C., Singh, D. P., Chari, S. T., & Goenka, A. H. (2021) · Medical Physics · doi:10.1002/mp.14782 · Cited by 41 · see citing works

Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey

Xu, L., Zhu, S., & Wen, N. (2022) · Physics in Medicine &amp; Biology · doi:10.1088/1361-6560/ac9cb3 · Cited by 35 · see citing works

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

Wang, Y., Tang, P., Zhou, Y., Shen, W., Fishman, E. K., & Yuille, A. L. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3060066 · Cited by 34 · see citing works

Pre-training in Medical Data: A Survey

Qiu, Y., Lin, F., Chen, W., & Xu, M. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1382-8 · Cited by 30 · see citing works

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

Shen, C., Wang, P., Roth, H. R., Yang, D., Xu, D., Oda, M., Wang, W., Fuh, C.-S., Chen, P.-T., Liu, K.-L., et al. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-90874-4_10 · Cited by 36 · see citing works

Fully Automated Placental Volume Quantification From<scp>3D</scp>Ultrasound for Prediction of Small‐for‐Gestational‐Age Infants

Schwartz, N., Oguz, I., Wang, J., Pouch, A., Yushkevich, N., Parameshwaran, S., Gee, J., Yushkevich, P., & Oguz, B. (2022) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15835 · Cited by 11 · see citing works

Vessel-Guided Reinforcement Learning-Based Refinement of 3D SegNet for Multi-Class Liver Segmentation

Wali, S., Khan, M. I., Samina, S., & Raza Sialvi, M. A. (2026) · 2026 11th International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc70586.2026.11686729