IP Library › Granted Patent US 12,260,622
Granted Patent B2
US 12,260,622 · App. 17/625,313 · Granted Mar 25, 2025

Systems, methods, and apparatuses for the generation of source models for transfer learning to application specific models used in the processing of medical imaging

Inventors: Zongwei Zhou (Tempe, AZ); Vatsal Sodha (San Jose, CA); Md Mahfuzur Rahman Siddiquee (Tempe, AZ); Ruibin Feng (Scottsdale, AZ); Nima Tajbakhsh (Los Angeles, CA); Jianming Liang (Scottsdale, AZ)
Assignee: Arizona Board of Regents on behalf of Arizona State University
G06V10/7747G06V10/776G06V10/82G06V10/98G06V2201/03
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,260,622
App. No.
17/625,313
Granted
Mar 25, 2025
Kind
B2
Abstract

Described herein are means for generating source models for transfer learning to application specific models used in the processing of medical imaging. In some embodiments, the method comprises: identifying a group of training samples, wherein each training sample in the group of training samples includes an image; for each training sample in the group of training samples: identifying an original patch of the image corresponding to the training sample; identifying one or more transformations to be applied to the original patch; generating a transformed patch by applying the one or more transformations to the identified patch; and training an encoder-decoder network using a group of transformed patches corresponding to the group of training samples, wherein the encoder-decoder network is trained to generate an approximation of the original patch from a corresponding transformed patch, and wherein the encoder-decoder network is trained to minimize a loss function that indicates a difference between the generated approximation of the original patch and the original patch. The source models significantly enhance the transfer learning performance for many medical imaging tasks including, but not limited to, disease/organ detection, classification, and segmentation. Other related embodiments are disclosed.

Claims (54)

1. A system comprising:

a memory to store instructions;

a set of one or more processors to execute the instructions stored in the memory to:

identify a group of unlabeled and unannotated training samples, wherein each unlabeled and unannotated training sample includes a medical image of a selected anatomical region of a body of a patient;

for each unlabeled and unannotated training sample in the group of unlabeled and unannotated training samples:

identify a patch that is a portion of the medial image corresponding to the unlabeled and unannotated training sample;

identify one or more transformations to be applied to the patch; and

generate a transformed patch by applying the one or more transformations to the patch; and

train a source model comprising an encoder-decoder network to learn anatomical patterns from the medical images of the selected anatomical region in a self-supervised manner using a group of transformed patches corresponding to the group of unlabeled and unannotated training samples, and without using labeled or annotated training samples, wherein the encoder-decoder network is trained to generate an approximation of the patch from a corresponding transformed patch, and wherein the encoder-decoder network is trained to minimize a loss function that indicates a difference between the generated approximation of the patch and the patch.

2. The system of claim 1 , wherein each medical image is a three-dimensional medical image.

3. The system of claim 1 , wherein the one or more transformations include changing an intensity value of each pixel in the patch.

4. The system of claim 1 , wherein the one or more transformations include for each pixel in the patch, changing a location of the pixel from a first location to a second location.

5. The system of claim 1 , wherein the one or more transformations include masking one or more portions of the patch.

6. The system of claim 1 , wherein identifying the one or more transformations to be applied to the patch comprises:

identifying a group of candidate transformations, wherein each transformation in the group of candidate transformations is associated with a probability that the transformation will be selected as one of the one or more transformations to be applied to the patch; and

selecting the one or more transformations based on the probability associated with each transformation in the group of candidate transformations.

7. The system of claim 1 , wherein the set of one or more processors further to execute instructions stored in the memory to:

train, using the trained source model, the application-specific target model to perform the task relating to the medical image of the selected anatomical region.

8. The system of claim 7 , wherein the set of one or more processors further to execute instructions stored in the memory to train, using the trained source model, the application-specific target model to perform the task relating to the medical image of the selected anatomical region, comprises instructions to identify portions of the medical image of the anatomical region that include a tumor or lesion or classify a medical image of the anatomical region as associated with one of a healthy-and a particular disease-state.

9. A non-transitory computer-readable medium containing computer executable instructions that, when executed by a processor, cause the processor to perform a method comprising:

identifying a group of unlabeled and unannotated training samples, wherein each unlabeled and unannotated training sample includes a medical image of a selected anatomical region;

for each unlabeled and unannotated training sample in the group of unlabeled and unannotated training samples:

identifying a patch that is a portion of the medical image corresponding to the unlabeled and unannotated training sample;

identifying one or more transformations to be applied to the patch; and

generating a transformed patch by applying the one or more transformations to the patch; and

training a source model comprising an encoder-decoder network to learn anatomical patterns from the medical images of the selected anatomical region in a self-supervised manner using a group of transformed patches corresponding to the group of unlabeled and unannotated training samples, and without using labeled or annotated training samples, wherein the encoder-decoder network is trained to generate an approximation of the patch from a corresponding transformed patch, and wherein the encoder-decoder network is trained to minimize a loss function that indicates a difference between the generated approximation of the patch and the patch.

10. The non-transitory computer-readable medium of claim 9 , wherein each medical image is a three-dimensional medical image.

11. The non-transitory computer-readable medium of claim 9 , wherein the one or more transformations include changing an intensity value of each pixel in the patch.

12. The non-transitory computer-readable medium of claim 9 , wherein the one or more transformations include for each pixel in the patch, changing a location of the pixel from a first location to a second location.

13. The non-transitory computer-readable medium of claim 9 , wherein the one or more transformations include masking one or more portions of the patch.

14. The non-transitory computer-readable medium of claim 9 , wherein identifying the one or more transformations to be applied to the patch comprises:

identifying a group of candidate transformations, wherein each transformation in the group of candidate transformations is associated with a probability that the transformation will be selected as one of the one or more transformations to be applied to the patch; and

selecting the one or more transformations based on the probability associated with each transformation in the group of candidate transformations.

15. A method for performing object detection, comprising:

initializing a neural network by appending a feature extraction backbone of a detection network to an encoder network of a pre-trained encoder-decoder network, wherein the pre-trained encoder-decoder network has been trained to generate an approximation of an input image that has been transformed using one or more image transformations;

identifying a group of unlabeled and unannotated training samples, wherein each unlabeled and unannotated training sample includes an image;

for each unlabeled and unannotated training sample in the group of unlabeled and unannotated training samples:

applying operations associated with each layer of the encoder network and the feature extraction backbone to the image corresponding to the unlabeled and unannotated training sample;

identifying one or more bounding boxes corresponding to the image associated with the unlabeled and unannotated training sample;

calculating a probability that a region inside each of the one or more bounding boxes includes a target object; and

calculating a detection error by comparing the probabilities associated with each region with ground truth values associated with the unlabeled and unannotated training sample; and

updating weights associated with the detection network to minimize the detection error.

16. The method of claim 15 , further comprising:

identifying a patch that is a portion of the image corresponding to the unlabeled and unannotated training sample;

identifying one or more transformations to be applied to the patch;

generating a transformed patch by applying the one or more transformations to the patch; and

training an encoder-decoder network to learn patterns from the images in a self-supervised manner using a group of transformed patches corresponding to the group of unlabeled and unannotated training samples, and without using labeled or annotated training samples, wherein the encoder-decoder network is trained to generate an approximation of the patch from a corresponding transformed patch, and wherein the encoder-decoder network is trained to minimize a loss function that indicates a difference between the generated approximation of the patch and the patch.

17. The method of claim 16 , wherein each image is a three-dimensional image.

18. The method of claim 16 , wherein the one or more transformations include changing an intensity value of each pixel in the patch.

19. The method of claim 16 , wherein the one or more transformations include for each pixel in the patch, changing a location of the pixel from a first location to a second location.

20. The method of claim 16 , wherein the one or more transformations include masking one or more portions of the patch.

21. The method of claim 16 , wherein identifying the one or more transformations to be applied to the patch comprises:

identifying a group of candidate transformations, wherein each transformation in the group of candidate transformations is associated with a probability that the transformation will be selected as one of the one or more transformations to be applied to the patch; and

selecting the one or more transformations based on the probability associated with each transformation in the group of candidate transformations.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 11, 2022
From: ZHOU, ZONGWEI; SODHA, VATSAL; RAHMAN SIDDIQUEE, MD MAHFUZUR; FENG, RUIBIN; TAJBAKHSH, NIMA; LIANG, JIANMING
To: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY
Reel/Frame 058620/0332 →
Continuity (2)
Provisional Application 62876502 · Jul 19, 2019
Related Publication 20220262105A1 · Aug 18, 2022
References Cited (99)
US 20160110632A1 · Kiraly · 2016 [cited by examiner]
US 20180114317A1 · Song · 2018 [cited by examiner]
US 20210326653A1 · Zhou et al. · 2021 [cited by applicant]
US 20210342646A1 · Feng et al. · 2021 [cited by applicant]
US 20210343014A1 · Haghighi et al. · 2021 [cited by applicant]
WO 2021016087A1 · 2021 [cited by applicant]
Mortenson, M.E., “Mathematics for computer graphics applications,” Industrial Press Inc., 1999. [cited by applicant]
Mundhenk, T. N. et al., “Improvements to Context Based Self-Supervised Learning.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 9339-9348, https://doi.org/10.1109/CVPR.2018.00973. [cited by applicant]
National Lung Screening Trial Research Team, “Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening,” New England Journal of Medicine, vol. 365, No. 5, 2011, pp. 395-409. [cited by applicant]
Noroozi, M. et al., “Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles,” European Conference on Computer Vision, 2016, Springer International Publishing, pp. 69-84. [cited by applicant]
Noroozi, M., et al, “Boosting Self-Supervised Learning via Knowledge Transfer,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 9359-9367. [cited by applicant]
Pan, S. J. et al., “A Survey on Transfer Learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, No. 10, 2010, pp. 1345-1359. [cited by applicant]
Pathak, D. et al., “Context Encoders: Feature Learning by Inpainting,” 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2536-2544, https://doi.org/10.1109/CVPR.2016.278. [cited by applicant]
Rawat, W. et al., “Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review,” Neural Computation, vol. 29, No. 9, 2017, pp. 2352-2449. [cited by applicant]
Ross, T. et al., “Exploiting the potential of unlabeled endoscopic video data with self-supervised learning,” International Journal of Computer Assisted Radiology and Surgery 13, 2018, pp. 925-933. [cited by applicant]
Roth, H.R. et al., “A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations,” in International Conference on Medical Image Computing and Computer-Assisted I… [cited by applicant]
Roth, H.R., et al., “Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation,” IEEE Transactions on Medical Imaging, vol. 35, No. 5, May 2016, pp. 1170-1181. [cited by applicant]
Sayed, N., et al., “Cross and Learn: Cross-Modal Self-supervision,” Pattern Recognition, GCPR 2018, Lecture Notes in Computer Science, vol. 11269, 2019, Springer International Publishing, pp. 228-243. [cited by applicant]
Setio, A.A.A. et al., “Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks,” IEEE Transactions on Medical Imaging vol. 35, No. 5, May 2016, pp. 1160-1169. [cited by applicant]
Setio, A.A.A. et al., “Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge,” Medical Image Analysis, vol. 42, 2017, pp. … [cited by applicant]
Shen, D., et al, “Deep Learning in Medical Image Analysis,” Annual Review of Biomedical Engineering, vol. 19, 2017, pp. 221-248. [cited by applicant]
Shin, H.C. et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Transactions on Medical Imaging, vol. 35, No. 5, 2016, pp. 128… [cited by applicant]
Spitzer, H. et al., “Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks,” Medical Image Computing and Computer Assisted Intervention—MICCAI 2018: 21st International Confe… [cited by applicant]
Sun, C. et al., “Revisiting Unreasonable Effectiveness of Data in Deep Learning Era,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 843-852. [cited by applicant]
Sun, W., et al, “Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis,” Computers in Biology and Medicine, vol. 89, 2017, pp. 530-539. [cited by applicant]
Tajbakhsh, N. et al., “Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation,” Medical Image Analysis, vol. 58, 2019, Article 101541, 13 pages. [cited by applicant]
Tajbakhsh, N. et al., “Computer-Aided Pulmonary Embolism Detection Using a Novel Vessel-aligned Multi-planar Image Representation and Convolutional Neural Networks,” Medical Image Computing and Computer-Assisted Interve… [cited by applicant]
Tajbakhsh, N. et al., “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?,” IEEE Transactions on Medical Imaging, vol. 35, No. 5, May 2016, pp. 1299-1312. [cited by applicant]
Tajbakhsh, N. et al., “Surrogate Supervision for Medical Image Analysis: Effective Deep Learning from Limited Quantities of Labeled Data,” IEEE 16th International Symposium on Biomedical Imaging, Venice, Italy, 2019, pp… [cited by applicant]
Tajbakhsh, N., et al., “Computer-aided detection and visualization of pulmonary embolism using a novel, compact and discriminative image representation,” Medical Image Analysis, vol. 58, 2019, 101541, 15 pages. [cited by applicant]
Tang, H., et al., “NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation,” Medical Image Computing and Computer Assisted Intervention—MICCAI 2019: 22nd International Conference, S… [cited by applicant]
Tang, Y., et al., “Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs,” Machine Learning in Medical Imaging: 9th International Workshop, … [cited by applicant]
Vincent, P., et al., “Stacked Denoising Autoencoders: Learning Ueful Representations in a Deep Network with a Local Denoising Criterion,” Journal of Machine Learning Research, vol. 11, No. 12, 2010, pp. 3371-3408. [cited by applicant]
Voulodimos, A., et al., “Deep Learning for Computer Vision: A Brief Review,” Computational Intelligence and Neuroscience, vol. 2018, Article 7068349, 13 pages. [cited by applicant]
Wang, H. et al., “Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non- small cell lung cancer from 18 F-FDG PET/CT images,” EJNMMI research 7(1), 2017, pp. 1-11. [cited by applicant]
Wang, X. et al., “ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases,” Proceedings of the IEEE Conference on Computer Vision an… [cited by applicant]
Weiss, K., et al., “A survey of transfer learning,” Journal of Big Data, vol. 3,Article No. 9, 2016, 40 pages. [cited by applicant]
Wu, B. et al., “Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction,” In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 1109-1113, IEEE. [cited by applicant]
Yosinski, J. et al., “How transferable are features in deep neural networks?” Advances in Neural Information Processing Systems, 27 (NIPS 2014), arXiv preprint arXiv:1411.1792. [cited by applicant]
Zhang, L. et al., “AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations Rather than Data,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 2542-… [cited by applicant]
Zhang, R., et al, “Colorful Image Colorization,” Computer Vision—ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, Oct. 11-14, 2016, Proceedings, Part III, Lecture Notes on Computer Science vol. 9907, 201… [cited by applicant]
Zhang, R., et al, “Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 645-654. [cited by applicant]
Zhang, T., et al, “Solving Large Scale Linear Prediction Problems Using Stochastic Gradient Descent Algorithms,” Proceedings of the 21st International Conference on Machine Learning, 2004, 8 pages. [cited by applicant]
Zhou, Z. et al., “Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally,” In 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 4761-4772. [cited by applicant]
Zhou, Z. et al., “Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis,” Medical Image Computing and Computer Assisted Intervention—MICCAI 2019: 22nd International Conference, Shenzhen, China, Oct. … [cited by applicant]
Zhou, Z., et al, “Integrating Active Learning and Transfer Learning for Carotid Intima-Media Thickness Video Interpretation,” Journal of Digital Imaging, vol. 32, 2019, pp. 290-299. [cited by applicant]
Zhou, Z., et al, “Models Genesis,” Medical Image Analysis, vol. 67, 2021, Article 101840, 23 pages. [cited by applicant]
Zhou, Z., et al, “UNet++: A Nested U-Net Architecture for Medical Image Segmentation,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2018, pp. 3-11, Springer, Cham. [cited by applicant]
Zhuang, X. et al., “Self-supervised Feature Learning for 3d Medical Images by Playing a Rubik's Cube,” Medical Image Computing and Computer Assisted Intervention—MICCAI 2019: 22nd International Conference, Shenzhen, Chi… [cited by applicant]
Alex, V. et al., Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation, Journal of Medical Imaging 4(4), 2017, pp. 041311-041311-16. [cited by applicant]
Ardila, D. et al., “End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography,” Nature Medicine 25(6), 2019, pp. 954-961. [cited by applicant]
Aresta, G. et al., “iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network,” Scientific Reports 9(1), 2019, 9 pages. [cited by applicant]
Armato III, S.G. et al., “The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans,” Medical Physics 38(2), 2011, pp. 915-931. [cited by applicant]
Bakas, S. et al., “Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge,” arXiv preprint arXiv:1811.02629, 2018, 2… [cited by applicant]
Bilic, P. et al., “The Liver Tumor Segmentation Benchmark (LiTS),” Medical Image Analysis, 84, 102680, arXiv preprint arXiv:1901.04056, 2019. [cited by applicant]
Buzug, T.M., “Computed Tomography,” Handbook of Medical Technology, 2011, Springer Berlin, Heidelberg, pp. 311-342. [cited by applicant]
Caron, M. et al., “Deep Clustering for Unsupervised Learning of Visual Features,” In Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 132-149. [cited by applicant]
Caron, M. et al., “Unsupervised Pre-Training of Image Features on Non-Curated Data,” Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 2959-2968. [cited by applicant]
Carreira, J. et al., “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset,” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (Jul. 2017), pp. 6299-6308. [cited by applicant]
Chen, L. et al., “Self-supervised learning for medical image analysis using image context restoration,” Medical Image Analysis, vol. 58, 2019, 12 pages, https://doi.org/10.1016/j.media.2019.101539. [cited by applicant]
Chen, S. et al., Med3D: Transfer Learning for 3d Medical Image Analysis. arXiv preprint arXiv:1904.00625, 2019, 12 pages. [cited by applicant]
Chen, T. et al., “Self-Supervised GANs via Auxiliary Rotation Loss,” arXiv: 1811.11212v2 [cs.LG], 2019, 15 pages. [cited by applicant]
Deng, J. et al., “ImageNet: A Large-Scale Hierarchical Image Database,” 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255. [cited by applicant]
Ding, Y. et al., “A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG Pet of the Brain,” Radiology, vol. 290, No. 2, 2019, pp. 456-464. [cited by applicant]
Doersch, C. et al., “Multi-task Self-Supervised Visual Learning,” ARXIV:1708.07860v1: [cs:CV], 2017, 13 pages. [cited by applicant]
Doersch, C. et al., “Unsupervised Visual Representation Learning by Context Prediction,” Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1422-1430, https://doi.org/10.1109/ICCV.2015.167. [cited by applicant]
Esteva, A.,. et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, 2017, pp. 115-118. [cited by applicant]
Feng, R., et al, “Self-supervised Learning: From Parts to Whole,” unpublished manuscript, 12 pages. [cited by applicant]
Forbes, G.B., “Human Body Composition: Growth, Aging, Nutrition, and Activity,” 2012, Springer Science & Business Media. [cited by applicant]
Gibson, E. et al., “NiftyNet: a deep-learning platform for medical imaging,” Computer Methods and Programs in Biomedicine, vol. 158, 2018, pp. 113-122. [cited by applicant]
Gibson, E., et al., “Automatic Multi-Organ Segmentation on Abdominal CT with Dense V-Networks,” IEEE Transactions on Medical Imaging, vol. 37, No. 8, 2018, pp. 1822-1834. [cited by applicant]
Glorot, X., et al., “Understanding the difficulty of training deep feed forward neural networks,” International Conference on Artificial Intelligence and Statistics (2010), pp. 249-256. [cited by applicant]
Goyal, P. et al., “Scaling and Benchmarking Self-Supervised Visual Representation Learning,” Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 6390-6399. [cited by applicant]
Greenspan, H. et al., “Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique”, IEEE Transactions on Medical Imaging, vol. 35, No. 5, May 2016, pp. 1153-1159. [cited by applicant]
Guan, Q., et al, “Multi-label chest X-ray image classification via category-wise residual attention learning,” Pattern Recognition Letters, 130, 2020, pp. 259-266. [cited by applicant]
Guendel, S., et al, “Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks,” arXiv: 1803.04565v1 [cs.CV], 2018, 9 pages. [cited by applicant]
Haghighi, F., et al, “Semantic Genesis,” unpublished manuscript, 12 pages. [cited by applicant]
He, K. et al., “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778. [cited by applicant]
He, K. et al., “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1026-1034. [cited by applicant]
Hendrycks, D. et al., “Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty,” Advances in Neural Information Processing Systems, 32, 2019, 12 pages. [cited by applicant]
Huang, G. et al., “Densely Connected Convolutional Networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2261-2269. [cited by applicant]
Hurst, R.T. et al., “Incidence of Subclinical Atherosclerosis as a Marker of Cardiovascular Risk in Retired Professional Football Players,” The American Journal of Cardiology, vol. 105, Issue 8, 2010, pp. 1107-1111. [cited by applicant]
Iizuka, S., et al., “Globally and Locally Consistent Image Completion,” ACM Transactions on Graphics, vol. 36, No. 4, 2017, Article 107, pp. 107:2-107:14. [cited by applicant]
International Search Report and Written Opinion for International Patent Application No. PCT/US20/42560, dated Oct. 7, 2020, 21 pages. [cited by applicant]
Ioffe, S., et al, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” Conference on Machine Learning, vol. 37, 2015, JMLR, pp. 448-456. [cited by applicant]
Irvin, J. et al. “CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, No. 01, 2019, pp. 590-597. [cited by applicant]
Jamaludin, A., et al, “Self-Supervised Learning for Spinal MRIs,” arXiv:1708.00367v1 [cs:CV], 2017, 8 pages. [cited by applicant]
Jing, L., et al, “Self-Supervised Visual Feature Learning with Deep Neural Networks: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, No. 11, 2021, pp. 4037-4058. [cited by applicant]
Kang, G., et al, “PatchShuffle Regularization,” arXiv preprint arXiv: 1707.07103v1 [cs.CV], 2017, 10 pages. [cited by applicant]
Kingma, D., et al, “Adam: A Method for Stochastic Optimization,” arXiv:1412.6980v8 [cs:CV], 2015, 15 pages. [cited by applicant]
Kolesnikov, A., et al, “Revisiting Self-Supervised Visual Representation Learning,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 1920-1929. [cited by applicant]
Krizhevsky, A., et al, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, vol. 25, 2012, 9 pages. [cited by applicant]
Litjens, G., et al, “A survey on deep learning in medical image analysis,” Medical Image Analysis vol. 42, 2017, pp. 60-88. [cited by applicant]
LiTS, 2017. Results of all submissions for liver segmentation. URL:https://competitions.codalab.org/competitions/17094#results, 7 pages. [cited by applicant]
Lu, L. et al., “Deep learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets,” Advances in Computer Vision and Pattern Recognition, 2017, Spr… [cited by applicant]
Luna, 2016. Results of all submissions for nodule false positive reduction URL:https://luna16.grand-challenge.org/results/, 2 pages. [cited by applicant]
Ma, Y., et al, “Multi-Attention Network for Thoracic Disease Classification and Localization,” ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 1378-1382. [cited by applicant]
Mahendran, A., et al, “Cross Pixel Optical-Flow Similarity for Self-Supervised Learning,” Computer Vision—ACCV 2018: 14th Asian Conference on Computer Vision, 2018, Lecture Notes in Computer Science, vol. 11365, 2019, S… [cited by applicant]
Menze, B.H., et al., “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),” IEEE Transactions on Medical Imaging, vol. 34, No. 10, 2015, pp. 1993-2024. [cited by applicant]