3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint

Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works

3D Slicer as an image computing platform for the Quantitative Imaging Network

Fedorov, A., Beichel, R., Kalpathy–Cramer, J., Finet, J., Fillion‐Robin, J., Pujol, S., Bauer, C., Jennings, D. L., Fennessy, F., Milan Sonka, A., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.05.001 · Cited by 9,095 · see citing works

Normal Table of Xenopus Laevis (Daudin)

Nieuwkoop, P. D., & Faber, J. (1958) · Copeia · doi:10.2307/1439568 · Cited by 4,542 · see citing works

Normal table of Xenopus laevis (Daudin)

Gurdon, J. B. (1995) · Trends in Genetics · doi:10.1016/s0168-9525(00)89129-5 · Cited by 2,888 · see citing works

Hypercolumns for object segmentation and fine-grained localization

Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511 · see citing works

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.00567 · Cited by 741 · see citing works

Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., & Biller, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.024 · Cited by 505 · see citing works

Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound

Milletarì, F., Ahmadi, S., Kroll, C., Plate, A., Rozanski, V. E., Maiostre, J., Levin, J., Dietrich, O., Ertl-Wagner, B. B., Bötzel, K., et al. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.04.002 · Cited by 394 · see citing works

XuvTools: free, fast and reliable stitching of large 3D datasets

Emmenlauer, M., Ronneberger, O., Ponti, A., Schwarb, P., Griffa, A., Filippi, A., Nitschke, R., Driever, W., & BURKHARDT, H. (2009) · Journal of Microscopy · doi:10.1111/j.1365-2818.2008.03094.x · Cited by 142 · see citing works

Deep End2End Voxel2Voxel Prediction

Tran, D., Bourdev, L. D., Fergus, R., Torresani, L., & Paluri, M. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.57 · Cited by 108 · see citing works

Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks

Seyedhosseini, M., Sajjadi, M., & Tasdizen, T. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.269 · Cited by 95 · see citing works

Inversin relays Frizzled-8 signals to promote proximal pronephros development

Lienkamp, S., Ganner, A., Boehlke, C., Schmidt, T., Arnold, S. J., Schäfer, T., Romaker, D., Schuler, J., Hoff, S., Powelske, C., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1013070107 · Cited by 56 · see citing works

Renovascular imaging in the NSF Era

Roditi, G., Maki, J. H., Oliveira, G. L. D. S., & Michaely, H. J. (2009) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21977 · Cited by 47 · see citing works

Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and UltrasoundPreprint

Milletarì, F., Ahmadi, S., Kroll, C., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.07014 · Cited by 31 · see citing works

Deep End2End Voxel2Voxel PredictionPreprint

Tran, D., Bourdev, L., Fergus, R., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06681 · Cited by 10 · 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

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477 · see citing works

Image segmentation using deep learning: A survey

Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796 · see citing works

Clinically applicable deep learning for diagnosis and referral in retinal disease

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639 · see citing works

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works

3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

Graham, B., Engelcke, M., & Maaten, L. V. D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00961 · Cited by 1,788 · see citing works

Medical image analysis using convolutional neural networks: a review

Anwar, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., & Khan, M. K. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1088-1 · Cited by 1,431 · see citing works

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101693 · Cited by 1,095 · 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

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 · doi:10.1109/tmi.2017.2743464 · Cited by 751 · see citing works

AI in Medical Imaging Informatics: Current Challenges and Future Directions

Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works

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 · doi:10.1038/s41598-019-52737-x · Cited by 684 · see citing works

Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., & Vercauteren, T. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.103 · Cited by 659 · see citing works

Loss odyssey in medical image segmentation

Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662 · see citing works

Self-supervised learning for medical image analysis using image context restoration

Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101539 · Cited by 575 · see citing works

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Mazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554 · see citing works

NiftyNet: a deep-learning platform for medical imaging

Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.025 · Cited by 552 · see citing works

Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal

Guan, S., Khan, A. A., Sikdar, S., & Chitnis, P. V. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2912935 · Cited by 538 · see citing works

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502 · see citing works

NAS-Unet: Neural Architecture Search for Medical Image Segmentation

Weng, Y., Zhou, T., Li, Y., & Qiu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2908991 · Cited by 487 · see citing works

A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images

Wang, G., Liu, X., Li, C., Xu, Z., Ruan, J., Zhu, H., Meng, T., Li, K., Huang, N., & Zhang, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3000314 · Cited by 469 · see citing works

RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

Jin, Q., Meng, Z., Sun, C., Cui, H., & Su, R. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.605132 · Cited by 468 · see citing works

Automated deep-neural-network surveillance of cranial images for acute neurologic events

Titano, J. J., Badgeley, M., Schefflein, J., Pain, M., Su, A., Cai, M., Swinburne, N., Zech, J., Kim, J., Bederson, J., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0147-y · Cited by 458 · see citing works

Confidence calibration and predictive uncertainty estimation for deep medical image segmentation

Mehrtash, A., Wells, W. M., Tempany, C. M., Abolmaesumi, P., & Kapur, T. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3006437 · Cited by 428 · see citing works

3D Shape Generation and Completion through Point-Voxel Diffusion

Zhou, L., Du, Y., & Wu, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00577 · Cited by 421 · see citing works

ResNet and its application to medical image processing: Research progress and challenges

Xu, W., Fu, Y.-L., & Zhu, D. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107660 · Cited by 416 · see citing works

AI in Health: State of the Art, Challenges, and Future Directions

Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398 · see citing works

Federated learning enables big data for rare cancer boundary detection

Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394 · see citing works

A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution

Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., et al. (2024) · Science · doi:10.1126/science.adk4858 · Cited by 361 · see citing works

Machine learning analysis of whole mouse brain vasculature

Todorov, M. I., Paetzold, J. C., Schoppe, O., Tetteh, G., Shit, S., Efremov, V., Todorov-Völgyi, K., Düring, M., Dichgans, M., Piraud, M., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0792-1 · Cited by 338 · see citing works

Cellular and Molecular Probing of Intact Human Organs

Zhao, S., Todorov, M. I., Cai, R., -Maskari, R. A., Steinke, H., Kemter, E., Mai, H., Rong, Z., Warmer, M., Stanic, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.030 · Cited by 292 · see citing works

Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans

Ghesu, F.-C., Georgescu, B., Zheng, Y., Grbic, S., Maier, A., Hornegger, J., & Comaniciu, D. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2782687 · Cited by 310 · see citing works

Single-cell transcriptomes and whole-brain projections of serotonin neurons in the mouse dorsal and median raphe nuclei

Ren, J., Isakova, A., Friedmann, D., Zeng, J., Grutzner, S. M., Pun, A., Zhao, G. Q., Kolluru, S. S., Wang, R., Lin, R., et al. (2019) · eLife · doi:10.7554/elife.49424 · Cited by 304 · see citing works

Predicting conversion to wet age-related macular degeneration using deep learning

Yim, J., Chopra, R., Spitz, T., Winkens, J., Obika, A., Kelly, C., Askham, H., Lukic, M., Huemer, J., Fasler, K., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0867-7 · Cited by 297 · see citing works

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

Seo, H., Badiei Khuzani, M., Vasudevan, V., Huang, C., Ren, H., Xiao, R., Jia, X., & Xing, L. (2020) · Medical Physics · doi:10.1002/mp.13649 · Cited by 300 · see citing works

A connectomic study of a petascale fragment of human cerebral cortex

Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., et al. (2021) · doi:10.1101/2021.05.29.446289 · Cited by 243 · see citing works

Automatic 3d bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Duan, J., Bello, G., Schlemper, J., Bai, W., Dawes, T. J. W., Biffi, C., de Marvao, A., Doumoud, G., O'Regan, D. P., & Rueckert, D. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2894322 · Cited by 227 · see citing works

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.009 · Cited by 226 · see citing works

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body

Pan, C., Schoppe, O., Parra-Damas, A., Cai, R., Todorov, M. I., Gondi, G., von Neubeck, B., Böğürcü-Seidel, N., Seidel, S., Sleiman, K., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.013 · Cited by 206 · see citing works

Dual-force convolutional neural networks for accurate brain tumor segmentation

Chen, S., Ding, C., & Liu, M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.009 · Cited by 195 · see citing works

3D multi-view convolutional neural networks for lung nodule classification

Kang, G., Liu, K., Hou, B., & Zhang, N. (2017) · PLOS ONE · doi:10.1371/journal.pone.0188290 · Cited by 191 · see citing works

Skin disease diagnosis with deep learning: a review

Li, H., Pan, Y., Zhao, J., & Zhang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.096 · Cited by 178 · see citing works

Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges

Siam, M., Elkerdawy, S., Jagersand, M., & Yogamani, S. (2017) · 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317714 · Cited by 178 · see citing works

M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer

Jang, J., & Hwang, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02006 · Cited by 133 · see citing works

MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning

Luo, X., Wang, G., Song, T., Zhang, J., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., & Zhang, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102102 · Cited by 126 · see citing works

A deep 3D residual<scp>CNN</scp>for false‐positive reduction in pulmonary nodule detection

Jin, H., Li, Z., Tong, R., & Lin, L. (2018) · Medical Physics · doi:10.1002/mp.12846 · Cited by 118 · see citing works

Synaptic Cleft Segmentation in Non-isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Heinrich, L., Funke, J., Pape, C., Nunez-Iglesias, J., & Saalfeld, S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00934-2_36 · Cited by 117 · see citing works

Volumetric memory network for interactive medical image segmentation

Zhou, T., Li, L., Bredell, G., Li, J., Unkelbach, J., & Konukoglu, E. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102599 · Cited by 109 · see citing works

Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning

Milz, S., Arbeiter, G., Witt, C., Abdallah, B., & Yogamani, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00062 · Cited by 107 · see citing works

Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods

Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3076844 · Cited by 105 · see citing works

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Alves, N., Schuurmans, M., Litjens, G., Bosma, J. S., Hermans, J., & Huisman, H. (2022) · Cancers · doi:10.3390/cancers14020376 · Cited by 92 · see citing works

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., Ha, S. M., Shukla, G., Flanders, A., Kotrotsou, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117081 · Cited by 84 · see citing works

Ascending neurons convey behavioral state to integrative sensory and action selection brain regions

Chen, C.-L., Aymanns, F., Minegishi, R., Matsuda, V. D. V., Talabot, N., Günel, S., Dickson, B. J., & Ramdya, P. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01281-z · Cited by 58 · see citing works

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Lee, K., Turner, N., Macrina, T., Wu, J., Lu, R., & Seung, H. S. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.04.001 · Cited by 52 · see citing works

Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis

Yazdan-Panah, A., Schmidt-Mengin, M., Ricigliano, V. A. G., Soulier, T., Stankoff, B., & Colliot, O. (2023) · NeuroImage: Clinical · doi:10.1016/j.nicl.2023.103368 · Cited by 58 · see citing works

A survey on deep learning in medical ultrasound imaging

Song, K., Feng, J., & Chen, D. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1398393 · Cited by 59 · see citing works

The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research

Pati, S., Baid, U., Edwards, B., Sheller, M. J., Foley, P., Anthony Reina, G., Thakur, S., Sako, C., Bilello, M., Davatzikos, C., et al. (2022) · Physics in Medicine &amp; Biology · doi:10.1088/1361-6560/ac9449 · Cited by 51 · see citing works

Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning

Luo, J., Molbay, M., Chen, Y., Horvath, I., Kadletz, K., Kick, B., Zhao, S., Al-Maskari, R., Singh, I., Ali, M., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02528-1 · Cited by 53 · see citing works

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound

Torrents-Barrena, J., Monill, N., Piella, G., Gratacós, E., Eixarch, E., Ceresa, M., & González Ballester, M. A. (2021) · Academic Radiology · doi:10.1016/j.acra.2019.11.006 · Cited by 42 · 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

Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound

Yang, X., Dou, H., Huang, R., Xue, W., Huang, Y., Qian, J., Zhang, Y., Luo, H., Guo, H., Wang, T., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3069663 · Cited by 32 · see citing works

An unsupervised domain adaptation brain CT segmentation method across image modalities and diseases

Dong, D., Fu, G., Li, J., Pei, Y., & Chen, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118016 · Cited by 30 · see citing works

Deep Learning-Based Computer-Aided Detection System for Automated Treatment Response Assessment of Brain Metastases on 3D MRI

Cho, J., Kim, Y. J., Sunwoo, L., Lee, G. P., Nguyen, T. Q., Cho, S. J., Baik, S. H., Bae, Y. J., Choi, B. S., Jung, C., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.739639 · Cited by 30 · see citing works

AuxNet: Auxiliary Tasks Enhanced Semantic Segmentation for Automated Driving

Chennupati, S., Sistu, G., Yogamani, S., & Rawashdeh, S. (2019) · Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications · doi:10.5220/0007684106450652 · Cited by 42 · see citing works

Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study

Zhou, Y., Mei, S., Wang, J., Xu, Q., Zhang, Z., Qin, S., Feng, J., Li, C., Xing, S., Wang, W., et al. (2024) · eClinicalMedicine · doi:10.1016/j.eclinm.2024.102772 · Cited by 26 · see citing works

Automatic evaluation of endometrial receptivity in three-dimensional transvaginal ultrasound images based on 3D U-Net segmentation

Wang, X., Bao, N., Xin, X., Tan, J., Li, H., Zhou, S., & Liu, H. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-1155 · Cited by 20 · see citing works

Glioblastoma surgery imaging-reporting and data system: validation and performance of the automated segmentation task

Bouget, D., Eijgelaar, R. S., Pedersen, A., Kommers, I., Ardon, H., Barkhof, F., Bello, L., Berger, M. S., Nibali, M. C., Furtner, J., et al. (2021) · Cancers · doi:10.3390/cancers13184674 · Cited by 19 · see citing works

Automatic Extraction of Hiatal Dimensions in 3-D Transperineal Pelvic Ultrasound Recordings

Williams, H., Cattani, L., Van Schoubroeck, D., Yaqub, M., Sudre, C., Vercauteren, T., D'Hooge, J., & Deprest, J. (2021) · Ultrasound in Medicine &amp; Biology · doi:10.1016/j.ultrasmedbio.2021.08.009 · Cited by 19 · see citing works

clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

Shit, S., Paetzold, J. C., Sekuboyina, A., Ezhov, I., Unger, A., Zhylka, A., Pluim, J. P. W., Bauer, U., & Menze, B. H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01629 · Cited by 60 · see citing works

Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis

Jalalifar, S. A., Soliman, H., Sahgal, A., & Sadeghi-Naini, A. (2022) · Cancers · doi:10.3390/cancers14205133 · Cited by 15 · see citing works

Learning Interactive Real-World Simulators

Yang, S., Du, Y., Ghasemipour, K., Tompson, J., Kaelbling, L., Schuurmans, D., & Abbeel, P. (2023) · arXiv · doi:10.48550/arxiv.2310.06114 · Cited by 12 · see citing works

A deep-learning framework reveals whole-body perturbations at cell level

Kaltenecker, D., Horvath, I., Al-Maskari, R., Chen, Y., Kolabas, Z. I., Hoeher, L., Todorov, M., Minde, D.-P., Kapoor, S., Turhan, S. G., et al. (2026) · Nature · doi:10.1038/s41586-026-10535-2 · Cited by 5 · see citing works

Video Diffusion Models: A Survey

Melnik, A., Ljubljanac, M., Lu, C., Yan, Q., Ren, W., & Ritter, H. (2024) · arXiv · doi:10.48550/arxiv.2405.03150 · Cited by 4 · 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

Generation of Training and Test Data Sets

Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4_12 · Cited by 2 · see citing works

Robust Recognition of Conversational Telephone Speech via Multi-condition Training and Data Augmentation

Málek, J., Žďánský, J., & Červa, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00794-2_35 · Cited by 8 · see citing works

Two Recognition Models for Thai Dancing Data Set

Tongphasook, C., & Chantrapornchai, C. (2019) · doi:10.1109/incit.2019.8912096 · Cited by 1 · see citing works

Untitled work

(2013) · Cited by 131 · see citing works

Empirical comparison of robustness of classifiers on IR imagery

Zhang, P., Peng, J., Zhang, K., et al. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.604163 · Cited by 2 · see citing works