DeepMedic for Brain Tumor Segmentation

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · 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

Identity mappings in deep residual networks

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741 · see citing works

The multimodal brain tumor image segmentation benchmark (BRATS)

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · cited 10× · see citing works

Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

Krähenbühl, P., & Koltun, V. (2012) · arXiv · doi:10.48550/arxiv.1210.5644 · Cited by 2,908 · see citing works

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735 · see citing works

A brain tumor segmentation framework based on outlier detection*1

Prastawa, M. W. (2004) · Medical Image Analysis · doi:10.1016/j.media.2004.06.007 · Cited by 637 · see citing works

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Maier, O., Menze, B. H., von der Gablentz, J., Häni, L., Heinrich‬, M. P., Liebrand, M., Winzeck, S., Basit, A., Bentley, P., Chen, L., et al. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.07.009 · Cited by 528 · see citing works

Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR

Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O. M., Das, T., Jena, R., & Price, S. J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33454-2_46 · Cited by 276 · see citing works

Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation

Mazzara, G. P., Velthuizen, R. P., Pearlman, J. L., Greenberg, H. M., & Wagner, H. (2004) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2004.01.026 · Cited by 216 · see citing works

Untitled work

(2014) · Cited by 188 · see citing works

Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI

Kamnitsas, K., Chen, L., Ledig, C., Rueckert, D., & Glocker, B. (2015) · Spiral (Imperial College London) · Cited by 107 · see citing works

An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation

Lyksborg, M., Puonti, O., Agn, M., & Larsen, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19665-7_17 · Cited by 104 · see citing works

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., & Davatzikos, C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-30858-6_13 · Cited by 101 · see citing works

Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random Fields

Zhao, X., Wu, Y., Song, G., Li, Z., Yong Fan, A., & Zhang, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-55524-9_8 · Cited by 93 · see citing works

Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

Alansary, A., Kamnitsas, K., Davidson, A., Khlebnikov, R., Rajchl, M., Malamateniou, C., Rutherford, M., Hajnal, J. V., Glocker, B., Rueckert, D., et al. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_68 · Cited by 77 · see citing works

Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model

Gooya, A., Pohl, K. M., Bilello, M., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23629-7_65 · Cited by 62 · see citing works

Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images

Parisot, S., Duffau, H., Chemouny, S., & Nikos Paragios, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33418-4_80 · Cited by 61 · see citing works

Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework

Zeng, K., Bakas, S., Sotiras, A., Akbari, H., Rozycki, M., Rathore, S., Pati, S., & Davatzikos, C. A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-55524-9_18 · Cited by 37 · see citing works

CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias

Meier, R., Knecht, U., Wiest, R., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-55524-9_10 · Cited by 16 · see citing works

Key challenges for delivering clinical impact with artificial intelligence

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works

Deep neural network models for computational histopathology: A survey

Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749 · 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

A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned

Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.028 · Cited by 348 · see citing works

Survey on deep learning for radiotherapy

Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works

Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study

Monteiro, M., Newcombe, V. F. J., Mathieu, F., Adatia, K., Kamnitsas, K., Ferrante, E., Das, T., Whitehouse, D., Rueckert, D., Menon, D. K., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30085-6 · Cited by 162 · see citing works

A new approach for brain tumor segmentation and classification based on score level fusion using transfer learning

Amin, J., Sharif, M., Yasmin, M., Saba, T., Anjum, M. A., & Fernandes, S. L. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1453-8 · Cited by 147 · 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

Autofocus Layer for Semantic Segmentation

Qin, Y., Kamnitsas, K., Ancha, S., Nanavati, J., Cottrell, G., Criminisi, A., & Nori, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00931-1_69 · Cited by 135 · see citing works

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study

Fathi Kazerooni, A., Arif, S., Madhogarhia, R., Khalili, N., Haldar, D., Bagheri, S., Familiar, A. M., Anderson, H., Haldar, S., Tu, W., et al. (2023) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdad027 · Cited by 55 · see citing works

Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma

Fathi Kazerooni, A., Akbari, H., Shukla, G., Badve, C., Rudie, J. D., Sako, C., Rathore, S., Bakas, S., Pati, S., Singh, A., et al. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00121 · Cited by 39 · see citing works

Deep Learning Strategies for Ultrasound in Pregnancy

Diniz, P. H. B., Yin, Y., & Collins, S. (2020) · EMJ Reproductive Health · doi:10.33590/emjreprohealth/20-00100 · Cited by 32 · see citing works

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data

Mitchell, J. R., Kamnitsas, K., Singleton, K. W., Whitmire, S. A., Clark-Swanson, K. R., Ranjbar, S., Rickertsen, C. R., Johnston, S. K., Egan, K. M., Rollison, D. E., et al. (2020) · Journal of Medical Imaging · doi:10.1117/1.jmi.7.5.055501 · Cited by 15 · see citing works

Depth-Specific Variational Scaling Method to Improve Accuracy of ResNet

Song, Y.-J., Jang, S., & Kim, K. (2021) · Journal of Korean institute of intelligent systems · doi:10.5391/jkiis.2021.31.4.338 · Cited by 2 · see citing works

Residual Attention Network for Brain Tumour Classification

Sashwini, A. P. S. T. (2019) · Unimas Institutional Repository (Universiti Malaysia Sarawak)

A Pipeline Defect Instance Segmentation System Based on SparseInst

Wang, N., Zhang, J., & Song, X. (2023) · Sensors · doi:10.3390/s23229019 · Cited by 10 · see citing works

A Residual Correction Approach for Semi-supervised Semantic Segmentation

Li, H., & Zheng, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88013-2_8 · Cited by 18 · see citing works

Deep Residual Networks and Weight InitializationPreprint

Taki, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02956 · Cited by 10 · see citing works