Deep learning in medical image analysis

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

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

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · 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

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Estimating the Dimension of a Model

Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · 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

Untitled work

(2014) · Cited by 33,419 · see citing works

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

Object recognition from local scale-invariant features

Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472 · see citing works

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · 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

Untitled work

(2010) · Cited by 14,134 · see citing works

Untitled work

(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works

Neural Networks for Pattern Recognition

Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287 · see citing works

The perceptron: A probabilistic model for information storage and organization in the brain.

Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280 · see citing works

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works

Untitled work

(2011) · Cited by 6,993 · see citing works

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542 · see citing works

Approximation capabilities of multilayer feedforward networks

Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091 · 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

A unified architecture for natural language processing: deep neural networks with multitask learning

Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works

Deep visual-semantic alignments for generating image descriptions

Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902 · see citing works

Training Products of Experts by Minimizing Contrastive Divergence

Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1× · see citing works

Greedy layer-wise training of deep networks

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works

Untitled work

(2013) · Cited by 3,740 · see citing works

Brain tumor segmentation with Deep Neural Networks

Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278 · 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

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works

Untitled work

(2013) · Cited by 2,056 · see citing works

Multiple Kernel Learning Algorithms

Gönen, M., & Alpaydın, E. (2011) · Cited by 1,651 · see citing works

Diffeomorphic demons: Efficient non-parametric image registration

Vercauteren, T. K., Pennec, X., Aymeric Perchant, B., & Ayache, N. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.040 · Cited by 1,589 · see citing works

Deformable medical image registration: a survey

Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587 · see citing works

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536 · see citing works

Auto-association by multilayer perceptrons and singular value decomposition

Bourlard, H., & Kamp, Y. (1988) · Biological Cybernetics · doi:10.1007/bf00332918 · Cited by 1,332 · see citing works

Deep Neural Networks for Object Detection

Szegedy, C., Toshev, A., & Erhan, D. (2013) · Cited by 1,313 · see citing works

Generating Text with Recurrent Neural Networks

Sutskever, I., Martens, J., & Hinton, G. E. (2011) · Cited by 1,266 · see citing works

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S. J., Wille, M. M. W., Naqibullah, M., Sanchez, C. I., & van Ginneken, B. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2536809 · Cited by 1,258 · see citing works

A Framework for Multiple-Instance Learning

Maron, O., & Lozano‐Pérez, T. (1997) · Cited by 1,232 · see citing works

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224 · see citing works

Exploring Strategies for Training Deep Neural Networks

Larochelle, H., Bengio, Y., Louradour, J., & Lamblin, P. (2009) · doi:10.1145/1577069.1577070 · Cited by 1,109 · see citing works

The "Wake-Sleep" Algorithm for Unsupervised Neural Networks

Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995) · Science · doi:10.1126/science.7761831 · Cited by 1,070 · see citing works

Deep Visual-Semantic Alignments for Generating Image Descriptions

Karpathy, A., & Fei-Fei, L. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2598339 · Cited by 1,048 · see citing works

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968 · see citing works

On optimization methods for deep learning

Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Le, Q. V., & Ng, A. Y. (2011) · International Conference on Machine Learning · Cited by 868 · 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

Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., & Madabhushi, A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2458702 · Cited by 854 · see citing works

Deeplysupervised netsPreprint

Lee, C., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2015) · Cited by 850 · see citing works

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817 · see citing works

Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans

Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., Huang, C.-S., Shen, D., & Chen, C.-M. (2016) · Scientific Reports · doi:10.1038/srep24454 · Cited by 755 · 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

Classification using discriminative restricted Boltzmann machines

Larochelle, H., & Bengio, Y. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390224 · Cited by 666 · see citing works

Multi-scale Convolutional Neural Networks for Lung Nodule Classification

Shen, W., Zhou, M., Yang, F., Yang, C., & Tian, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_46 · Cited by 635 · see citing works

Deep learning for neuroimaging: a validation study

Plis, S., Hjelm, D., Salakhutdinov, R., Allen, E. A., Bockholt, H. J., Long, J. D., Johnson, H. J., Paulsen, J. S., Turner, J. A., & Calhoun, V. D. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00229 · Cited by 617 · see citing works

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 · doi:10.1109/tmi.2015.2482920 · Cited by 608 · cited 2× · see citing works

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

Initiative, T. A. D. N., Suk, H., Lee, S., & Shen, D. G. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0687-3 · Cited by 523 · 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

Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data

Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J., & Leach, M. O. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.277 · Cited by 488 · see citing works

Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis

Li, R., Zhang, W., Suk, H., Wang, L., Jiang, L., Shen, D. G., & Ji, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_39 · Cited by 480 · see citing works

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation

Brosch, T., Tang, L. Y. W., Yoo, Y., Li, D. K. B., Traboulsee, A., & Tam, R. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528821 · Cited by 460 · see citing works

Learning Deep Generative Models

Salakhutdinov, R. (2015) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-010814-020120 · Cited by 433 · see citing works

A benchmark for comparison of dental radiography analysis algorithms

Wang, C., Huang, C.-T., Lee, J., et al. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.02.004 · Cited by 404 · see citing works

Adaptive, template moderated, spatially varying statistical classification

Warfield, S. K., Kaus, M. R., Jolesz, F. A., & Kikinis, R. (2000) · Medical Image Analysis · doi:10.1016/s1361-8415(00)00003-7 · Cited by 402 · see citing works

DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting

Ou, Y., Sotiras, A., Nikos Paragios, A., & Davatzikos, C. A. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.07.002 · Cited by 399 · see citing works

Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

Shin, H.-C., Roberts, K. E., Lü, L., Demner‐Fushman, D., Yao, J., & Summers, R. M. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.274 · Cited by 390 · see citing works

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

Kim, J., Calhoun, V. D., Shim, E., & Lee, J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.018 · Cited by 354 · see citing works

Automatic segmentation of MR images of the developing newborn brain

Prastawa, M. W., Gilmore, J. H., Lin, W., & Gerig, G. (2005) · Medical Image Analysis · doi:10.1016/j.media.2005.05.007 · Cited by 328 · see citing works

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

Suk, H., Chong-yaw Wee, A., Lee, S., & Shen, D. G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.005 · Cited by 311 · see citing works

Fully convolutional networks for multi-modality isointense infant brain image segmentation

Nie, D., Wang, L., Gao, Y., & Shen, D. (2016) · 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493515 · Cited by 304 · see citing works

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., de Jong, P. A., Prokop, M., & Ginneken, B. V. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.08.001 · Cited by 289 · see citing works

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

Guorong Wu, A., Kim, M., Wang, Q., Munsell, B. C., & Shen, D. G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2496253 · Cited by 277 · see citing works

Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks

Chen, H., Dou, Q., Wang, X., Qin, J., & Heng, P. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10140 · Cited by 264 · see citing works

Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Gao, M., Bağcı, U., Lü, L., Wu, A., Buty, M., Shin, H.-C., Roth, H. R., Papadakis, G. Z., Depeursinge, A., Summers, R. M., et al. (2016) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2015.1124249 · Cited by 259 · see citing works

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Guo, Y., Gao, Y., & Shen, D. G. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2508280 · Cited by 254 · see citing works

Manifold Learning of Brain MRIs by Deep Learning

Initiative, A. D. N., Brosch, T., & Tam, R. C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_78 · Cited by 236 · see citing works

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

Wang, L., Gao, Y., Shi, F., Li, G., Gilmore, J. H., Lin, W., & Shen, D. G. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.042 · Cited by 231 · see citing works

Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition

Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., Metaxas, D., & Zhou, X. S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2524985 · Cited by 223 · see citing works

Automatic segmentation of newborn brain MRI

Weisenfeld, N. I., & Warfield, S. K. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.04.068 · Cited by 205 · see citing works

Automatic segmentation and reconstruction of the cortex from neonatal MRI

Xue, H., Srinivasan, L., Jiang, S., Rutherford, M., Edwards, A. D., Rueckert, D., & Hajnal, J. V. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.030 · Cited by 204 · see citing works

Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network

Xie, Y., Xing, F., Kong, X., Su, H., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_43 · Cited by 204 · see citing works

Deep Neural Networks in Machine Translation: An Overview

Zhang, J., & Zong, C. (2015) · IEEE Intelligent Systems · doi:10.1109/mis.2015.69 · Cited by 199 · see citing works

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) · doi:10.1109/isbi.2015.7163826 · Cited by 188 · see citing works

A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

Farag, A. A., Lü, L., Roth, H. R., Liu, J., Turkbey, E. B., & Summers, R. M. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2624198 · Cited by 185 · see citing works

Segmentation of neonatal brain MR images using patch-driven level sets

Wang, L., Shi, F., Li, G., Gao, Y., Lin, W., Gilmore, J. H., & Shen, D. G. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.008 · Cited by 173 · see citing works

Natural Image Bases to Represent Neuroimaging Data

Gupta, A., Ayhan, M. S., & Maida, A. S. (2013) · Cited by 170 · see citing works

Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis

Initiative, T. A. D. N., Suk, H., Lee, S., & Shen, D. G. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1059-y · Cited by 164 · see citing works

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data

Munsell, B. C., Chong-yaw Wee, A., Keller, S. S., Weber, B., Elger, C. E., da Silva, L. A. T., Nesland, T., Styner, M. A., Shen, D. G., & Bonilha, L. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.008 · Cited by 164 · see citing works

Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation

Liao, S., Gao, Y., Oto, A., & Shen, D. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_32 · Cited by 157 · see citing works

Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study

Maier, O., Schröder, C., Forkert, N. D., Martinetz, T., & Handels, H. (2015) · PLOS ONE · doi:10.1371/journal.pone.0145118 · Cited by 150 · see citing works

Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images

Wu, G., Kim, M., Wang, Q., Gao, Y., Liao, S., & Shen, D. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_80 · Cited by 149 · see citing works

Marginal space deep learning: efficient architecture for volumetric image parsing

Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., & Comaniciu, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538802 · Cited by 142 · see citing works

Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines

van Tulder, G., & de Bruijne, M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526687 · Cited by 142 · see citing works

Automatic segmentation of neonatal images using convex optimization and coupled level sets

Wang, L., Shi, F., Lin, W., Gilmore, J. H., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.064 · Cited by 130 · see citing works

Morphology-driven automatic segmentation of MR images of the neonatal brain

Gui, L., Lisowski, R., Faundez, T., Hüppi, P. S., Lazeyras, F., & Kocher, M. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.07.006 · Cited by 114 · see citing works

Learning-based deformable registration of MR brain images

Wu, G., Qi, F., & Shen, D. G. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879320 · Cited by 106 · see citing works

Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

Su, H., Xing, F., Kong, X., Xie, Y., Zhang, S., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_46 · Cited by 90 · see citing works

Automated bone segmentation from dental CBCT images using patch‐based sparse representation and convex optimization

Wang, L., Chen, K. C., Gao, Y., Shi, F., Liao, S., Li, G., Shen, S. G., Yan, J., Lee, P. K. M., Chow, B., et al. (2014) · Medical Physics · doi:10.1118/1.4868455 · Cited by 89 · see citing works

Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images

Xie, Y., Kong, X., Xing, F., Liu, F., Su, H., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_45 · Cited by 79 · see citing works

Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning

Shao, Y., Gao, Y., Guo, Y., Shi, Y., Yang, X., & Shen, D. G. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2305691 · Cited by 64 · see citing works

Medical imaging

Brody, H. (2013) · Nature · doi:10.1038/502s81a · Cited by 61 · see citing works

S‐HAMMER: Hierarchical attribute‐guided, symmetric diffeomorphic registration for MR brain images

Wu, G., Kim, M., Wang, Q., et al. (2013) · Human Brain Mapping · doi:10.1002/hbm.22233 · Cited by 53 · see citing works

Deep models for brain EM image segmentation: novel insights and improved performance

Fakhry, A., Peng, H., & Ji, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw165 · Cited by 49 · see citing works

Deep Learning in Diagnosis of Brain Disorders

Suk, H., Shen, D. G., & Initiative, A. D. N. (2015) · Trends in augmentation of human performance · doi:10.1007/978-94-017-7239-6_14 · Cited by 46 · see citing works

Medical Imaging

Ching, W., & Ou, K. (2018) · Cited by 45 · see citing works

Bodypart Recognition Using Multi-stage Deep Learning

Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Metaxas, D., & Zhou, X. S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_35 · Cited by 39 · see citing works

A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set

Liu, F., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_42 · Cited by 33 · see citing works

Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image AnnotationPreprint

Shin, H.-C., Roberts, K. E., Lü, L., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.08486 · Cited by 33 · see citing works

New brain atlas—Mapping the human brain in vivo with 7.0 T MRI and comparison with postmortem histology: Will these images change modern medicine?

Cho, Z., Kim, Y.-B., Han, J., et al. (2008) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.20143 · Cited by 33 · see citing works

Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction

Chen, Y., Juttukonda, M. R., Su, Y., et al. (2014) · Radiology · doi:10.1148/radiol.14140810 · Cited by 30 · see citing works

Unsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR Images

Kim, M., Guorong Wu, A., & Shen, D. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02267-3_1 · Cited by 28 · see citing works

Multi-Tissue Decomposition of Diffusion MRI Signals via L0 Sparse-Group Estimation

Yap, P., Zhang, Y., & Shen, D. G. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2588328 · Cited by 22 · see citing works

Automatic Prostate MR Image Segmentation with Sparse Label Propagation and Domain-Specific Manifold Regularization

Liao, S., Gao, Y., Shi, Y., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38868-2_43 · Cited by 19 · see citing works

A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch LabelingPreprint

Farag, A. A., Lü, L., Roth, H. R., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.06236 · Cited by 12 · 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

Artificial intelligence in radiology

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works

An overview of deep learning in medical imaging focusing on MRI

Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688 · see citing works

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328 · see citing works

Deep multimodal learning: a survey on recent advances and trends

Ramachandram, D., & Taylor, G. W. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2738401 · Cited by 1,139 · see citing works

MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification

Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., & Ni, B. (2023) · Scientific Data · doi:10.1038/s41597-022-01721-8 · Cited by 1,043 · see citing works

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035 · see citing works

Domain adaptation for medical image analysis: A survey

Guan, H., & Liu, M. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3117407 · Cited by 1,057 · see citing works

Machine Learning Methods for Histopathological Image Analysis

Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937 · see citing works

Current Applications and Future Impact of Machine Learning in Radiology

Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P. V., Brink, J. A., & Dreyer, K. J. (2018) · Radiology · doi:10.1148/radiol.2018171820 · Cited by 800 · see citing works

Deep Learning in Medical Ultrasound Analysis: A Review

Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · 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

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond

Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works

Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.026 · Cited by 710 · see citing works

Applications for deep learning in ecology

Christin, S., Hervet, É., & Lecomte, N. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13256 · Cited by 724 · 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

Deep learning in medical image registration: a review

Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab843e · Cited by 661 · see citing works

Untitled work

Giger (2018) · doi:10.1016/j.jacr.2017.12.028 · Cited by 634 · see citing works

A survey on active learning and human-in-the-loop deep learning for medical image analysis

Budd, S., Robinson, E. C., & Kainz, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102062 · Cited by 606 · see citing works

Machine learning in cardiovascular medicine: are we there yet?

Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018) · Heart · doi:10.1136/heartjnl-2017-311198 · Cited by 555 · see citing works

Artificial Intelligence and Digital Pathology: Challenges and Opportunities

Tizhoosh, H. R., & Pantanowitz, L. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_53_18 · Cited by 544 · see citing works

Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI

Lian, C., Liu, M., Zhang, J., & Shen, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889096 · Cited by 544 · 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

Machine learning in medical applications: a review of state-of-the-art methods

Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105458 · Cited by 542 · 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

Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results

Li, X., Gu, Y., Dvornek, N., Staib, L. H., Ventola, P., & Duncan, J. S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101765 · Cited by 480 · see citing works

Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning

Tiu, E., Talius, E., Patel, P., Langlotz, C. P., Ng, A. Y., & Rajpurkar, P. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00936-9 · Cited by 442 · see citing works

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413 · see citing works

Deep learning in mental health outcome research: a scoping review

Su, C., Xu, Z., Pathak, J., & Wang, F. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0780-3 · Cited by 368 · see citing works

Deep learning in drug discovery: opportunities, challenges and future prospects

Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353 · see citing works

Economics of artificial intelligence in healthcare: Diagnosis vs. treatment

Khanna, N. N., Maindarkar, M. A., Viswanathan, V., Fernandes, J. F. E., Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., Kolluri, R., et al. (2022) · Healthcare · doi:10.3390/healthcare10122493 · Cited by 347 · see citing works

Brain Imaging Generation with Latent Diffusion Models

Pinaya, W. H. L., Tudosiu, P.-D., Dafflon, J., Da Costa, P. F., Fernandez, V., Nachev, P., Ourselin, S., & Cardoso, M. J. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-18576-2_12 · Cited by 395 · see citing works

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

Christopher, M., Belghith, A., Bowd, C., Proudfoot, J. A., Goldbaum, M. H., Weinreb, R. N., Girkin, C. A., Liebmann, J. M., & Zangwill, L. M. (2018) · Scientific Reports · doi:10.1038/s41598-018-35044-9 · Cited by 328 · see citing works

SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network

Jin, Y., Dou, Q., Chen, H., Yu, L., Qin, J., Fu, C.-W., & Heng, P.-A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2787657 · Cited by 316 · 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

An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery

Magesh, P. R., Myloth, R. D., & Tom, R. J. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104041 · Cited by 295 · see citing works

Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection.

Liu, F., Zhou, Z., Samsonov, A., Blankenbaker, D., Larison, W., Kanarek, A., Lian, K., Kambhampati, S., & Kijowski, R. (2018) · Radiology · doi:10.1148/radiol.2018172986 · Cited by 297 · see citing works

Automatic breast ultrasound image segmentation: a survey

Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.012 · Cited by 284 · see citing works

Machine learning for medical ultrasound: status, methods, and future opportunities

Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1517-0 · Cited by 262 · see citing works

Deep learning for electronic health records: A comparative review of multiple deep neural architectures

Ayala Solares, J. R., Diletta Raimondi, F. E., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Pinho Gomes, A. C., Payberah, A. H., Zottoli, M., Nazarzadeh, M., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103337 · Cited by 247 · see citing works

Content-based brain tumor retrieval for MR images using transfer learning

Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., & Lu, J. (2019) · IEEE Access · doi:10.1109/access.2019.2892455 · Cited by 246 · 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

The state of the art of deep learning models in medical science and their challenges

Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00694-1 · Cited by 222 · see citing works

Review of multimodal machine learning approaches in healthcare

Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102690 · Cited by 235 · see citing works

Deep Learning to Classify Radiology Free-Text Reports

Chen, M. C., Ball, R. L., Yang, L., Moradzadeh, N., Chapman, B. E., Larson, D. B., Langlotz, C. P., Amrhein, T. J., & Lungren, M. P. (2018) · Radiology · doi:10.1148/radiol.2017171115 · Cited by 217 · see citing works

Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages

Nie, D., Lu, J., Zhang, H., Adeli, E., Wang, J., Yu, Z., Liu, L., Wang, Q., Wu, J., & Shen, D. (2019) · Scientific Reports · doi:10.1038/s41598-018-37387-9 · Cited by 213 · see citing works

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP

Yushkevich, P. A., Pashchinskiy, A., Oguz, I., Mohan, S., Schmitt, J. E., Stein, J. M., Zukić, D., Vicory, J., McCormick, M., Yushkevich, N., et al. (2019) · Neuroinformatics · doi:10.1007/s12021-018-9385-x · Cited by 175 · see citing works

BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights

Benhammou, Y., Achchab, B., Herrera, F., & Tabik, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.09.044 · Cited by 170 · see citing works

Ultrasound Standard Plane Detection Using a Composite Neural Network Framework

Chen, H., Wu, L., Dou, Q., Qin, J., Li, S., Cheng, J.-Z., Ni, D., & Heng, P.-A. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2685080 · Cited by 164 · see citing works

Artificial intelligence system approaching neuroradiologist-level differential diagnosis accuracy at brain MRI

Rauschecker, A. M., Rudie, J. D., Xie, L., Wang, J., Duong, M. T., Botzolakis, E. J., Kovalovich, A. M., Egan, J., Cook, T. C., Bryan, R. N., et al. (2020) · Radiology · doi:10.1148/radiol.2020190283 · Cited by 148 · see citing works

Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

Zhou, K., Xiao, Y., Yang, J., Cheng, J., Liu, W., Luo, W., Gu, Z., Liu, J., & Gao, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58565-5_22 · Cited by 146 · see citing works

Convolutional neural networks for Alzheimer’s disease detection on MRI images

Ebrahimi, A., & Luo, S. (2021) · Journal of Medical Imaging · doi:10.1117/1.jmi.8.2.024503 · Cited by 133 · see citing works

A bird's-eye view of deep learning in bioimage analysis

Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126 · see citing works

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Moguilner, S., Baez, S., Hernandez, H., Migeot, J., Legaz, A., Gonzalez-Gomez, R., Farina, F. R., Prado, P., Cuadros, J., Tagliazucchi, E., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03209-x · Cited by 128 · 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

Towards automated semantic segmentation in prenatal volumetric ultrasound

Yang, X., Yu, L., Li, S., Wen, H., Luo, D., Bian, C., Qin, J., Ni, D., & Heng, P.-A. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2858779 · Cited by 115 · see citing works

Fusion of deep learning models of MRI scans, Mini–Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment

Qiu, S., Chang, G. H., Panagia, M., Gopal, D. M., Au, R., & Kolachalama, V. B. (2018) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.08.013 · Cited by 110 · see citing works

Automation and artificial intelligence in the clinical laboratory

Naugler, C., & Church, D. L. (2019) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2018.1561640 · Cited by 112 · see citing works

Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images

Behzadi-khormouji, H., Rostami, H., Salehi, S., Derakhshande-Rishehri, T., Masoumi, M., Salemi, S., Keshavarz, A., Gholamrezanezhad, A., Assadi, M., & Batouli, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105162 · Cited by 112 · see citing works

Artificial Intelligence in Imaging: The Radiologist’s Role

Rubin, D. L. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.05.036 · Cited by 110 · see citing works

Free lunch for testing: fuzzing deep-learning libraries from open source

Wei, A., Deng, Y., Yang, C., & Zhang, L. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510041 · Cited by 106 · see citing works

Generating synthetic data for medical imaging

Koetzier, L. R., Wu, J., Mastrodicasa, D., Lutz, A., Chung, M., Koszek, W. A., Pratap, J., Chaudhari, A. S., Rajpurkar, P., Lungren, M. P., et al. (2024) · Radiology · doi:10.1148/radiol.232471 · Cited by 111 · see citing works

The Project Baseline Health Study: a step towards a broader mission to map human health

Arges, K., Assimes, T., Bajaj, V., Balu, S., Bashir, M. R., Beskow, L., Blanco, R., Califf, R., Campbell, P., Carin, L., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0290-y · Cited by 82 · see citing works

Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy

Carneiro, G., Zorron Cheng Tao Pu, L., Singh, R., & Burt, A. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101653 · Cited by 76 · see citing works

Multiclass classification of brain tumors using a novel CNN architecture

Kibriya, H., Masood, M., Nawaz, M., & Nazir, T. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12977-y · Cited by 72 · see citing works

Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works

Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens

E. Ihongbe, I., Fouad, S., F. Mahmoud, T., Rajasekaran, A., & Bhatia, B. (2024) · PLOS ONE · doi:10.1371/journal.pone.0308758 · Cited by 75 · see citing works

Toward an interpretable Alzheimer’s disease diagnostic model with regional abnormality representation via deep learning

Lee, E., Choi, J.-S., Kim, M., & Suk, H.-I. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116113 · Cited by 61 · see citing works

Computing schizophrenia: ethical challenges for machine learning in psychiatry

Starke, G., De Clercq, E., Borgwardt, S., & Elger, B. S. (2021) · Psychological Medicine · doi:10.1017/s0033291720001683 · Cited by 62 · see citing works

CXR-LLaVA: a multimodal large language model for interpreting chest X-ray images

Lee, S., Youn, J., Kim, H., Kim, M., & Yoon, S. H. (2025) · European Radiology · doi:10.1007/s00330-024-11339-6 · Cited by 76 · see citing works

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis

de Jong, G., Bijlsma, E., Meulstee, J., Wennen, M., van Lindert, E., Maal, T., Aquarius, R., & Delye, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-72143-y · Cited by 62 · see citing works

Secure and robust machine learning for healthcare: A survey

Qayyum, A., Qadir, J., Bilal, M., & Al-Fuqaha, A. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3013489 · Cited by 59 · see citing works

Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging

Hsu, S.-T., Su, Y.-J., Hung, C.-H., Chen, M.-J., Lu, C.-H., & Kuo, C.-E. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02047-6 · 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

Ros‐NET: A deep convolutional neural network for automatic identification of rosacea lesions

Binol, H., Plotner, A., Sopkovich, J., Kaffenberger, B., Niazi, M. K. K., & Gurcan, M. N. (2020) · Skin Research and Technology · doi:10.1111/srt.12817 · Cited by 56 · see citing works

Generative Artificial Intelligence in Anatomic Pathology

Brodsky, V., Ullah, E., Bychkov, A., Song, A. H., Walk, E. E., Louis, P., Rasool, G., Singh, R. S., Mahmood, F., Bui, M. M., et al. (2025) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2024-0215-ra · Cited by 48 · see citing works

Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images

Li, P., Zhao, H., Liu, P., & Cao, F. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02242-5 · Cited by 47 · see citing works

Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology

You, S., Sun, Y., Yang, L., Park, J., Tu, H., Marjanovic, M., Sinha, S., & Boppart, S. A. (2019) · npj Precision Oncology · doi:10.1038/s41698-019-0104-3 · Cited by 46 · see citing works

Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept

Skarping, I., Larsson, M., & Förnvik, D. (2022) · European Radiology · doi:10.1007/s00330-021-08306-w · Cited by 44 · see citing works

Differentiating Dementia with Lewy Bodies and Alzheimer's Disease by Deep Learning to Structural MRI

Nemoto, K., Sakaguchi, H., Kasai, W., Hotta, M., Kamei, R., Noguchi, T., Minamimoto, R., Arai, T., & Asada, T. (2021) · Journal of Neuroimaging · doi:10.1111/jon.12835 · Cited by 37 · see citing works

The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology

Haller, S., Van Cauter, S., Federau, C., Hedderich, D. M., & Edjlali, M. (2022) · Neuroradiology · doi:10.1007/s00234-021-02890-w · Cited by 33 · see citing works

A Pseudo-Siamese Feature Fusion Generative Adversarial Network for Synthesizing High-Quality Fetal Four-Chamber Views

Qiao, S., Pan, S., Luo, G., Pang, S., Chen, T., Singh, A. K., & Lv, Z. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3143319 · Cited by 32 · see citing works

Deep Reinforcement Active Learning for Medical Image Classification

Wang, J., Yan, Y., Zhang, Y., Cao, G., Yang, M., & Ng, M. K. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59710-8_4 · Cited by 31 · see citing works

Objective Assessment of Pathological Voice Using Artificial Intelligence Based on the GRBAS Scale

Kojima, T., Fujimura, S., Hasebe, K., Okanoue, Y., Shuya, O., Yuki, R., Shoji, K., Hori, R., Kishimoto, Y., & Omori, K. (2024) · Journal of Voice · doi:10.1016/j.jvoice.2021.11.021 · Cited by 27 · see citing works

The Desire of Medical Students to Integrate Artificial Intelligence Into Medical Education: An Opinion Article

Frommeyer, T. C., Fursmidt, R. M., Gilbert, M. M., & Bett, E. S. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.831123 · Cited by 25 · see citing works

Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression

Pei, Y., Zhang, Y., Qin, H., Ma, G., Guo, Y., Xu, T., & Zha, H. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-67558-9_14 · Cited by 25 · see citing works

Patient-Generated Health Photos and Videos Across Health and Well-being Contexts: Scoping Review

Ploderer, B., Rezaei Aghdam, A., & Burns, K. (2022) · Journal of Medical Internet Research · doi:10.2196/28867 · Cited by 18 · see citing works

Deep learning applications in vascular dementia using neuroimaging

Dong, C., & Hayashi, S. (2024) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000920 · Cited by 13 · see citing works

A Hybrid Model for Fetal Growth Restriction Assessment by Automatic Placental Radiomics on <scp>T2</scp>‐Weighted <scp>MRI</scp> and Multifeature Fusion

Li, R., Song, F., Zhou, Q., Wu, W., Cao, Y., Zhang, G., Qian, Z., & Wang, L. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29399 · Cited by 10 · see citing works

Multimodal convolutional neural networks for the prediction of acute kidney injury in the intensive care

van Slobbe, R., Herrmannova, D., Boeke, D. J., Lima-Walton, E. S., Abu-Hanna, A., & Vagliano, I. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105815 · Cited by 7 · see citing works

Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum

Waszczuk, D., Manikandan, V., Wong, B. L., Martin, F., Bhargava, N., Mondal, A., Loy, M., Strnad, D., Panchatcharam, M., Sadanala, G., et al. (2026) · Cureus · doi:10.7759/cureus.106592

PExy: The Other Side of Exploit Kits

Maio, G. D., Kapravelos, A., Shoshitaishvili, Y., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08509-8_8 · Cited by 24 · see citing works

AEMB: An Automated Exploit Mitigation Bypassing Solution

Wang, R., Pan, Z., Shi, F., et al. (2021) · Applied Sciences · doi:10.3390/app11209727 · Cited by 4 · see citing works

AEG: Automatic Exploit Generation

(2018) · doi:10.1184/r1/6468296.v1 · Cited by 205 · see citing works

Automated Crash Analysis and Exploit Generation with Extendable Exploit Model

Liu, Z., Wang, Z., Zhang, Y., et al. (2022) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc55868.2022.00017 · Cited by 4 · see citing works

[Research progress in medical imaging based on deep learning of neural network].

Mu, C., & Li, G. (2019) · PubMed · doi:10.3760/cma.j.issn.1002-0098.2019.07.011 · Cited by 7 · see citing works