Multi-Task Knowledge Distillation for Eye Disease Prediction
暂无分享,去创建一个
Sahil Chelaramani | Vipul Agarwal | Ranya Habash | Manish Gupta | Prashant Gupta | Manish Gupta | Prashant Gupta | Vipul Agarwal | Ranya Habash | Sahil Chelaramani
[1] Omer Levy,et al. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.
[2] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[4] Bunyarit Uyyanonvara,et al. An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation , 2012, IEEE Transactions on Biomedical Engineering.
[5] Hassan Ghasemzadeh,et al. Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher , 2019, ArXiv.
[6] Harsha L. Rao,et al. Accuracy Of Ordinary Least Squares And Empirical Bayes Estimates Of Short Term Visual Field Progression Rates To Predict Long Term Outcomes In Glaucoma , 2012 .
[7] J. W. Harbour. Molecular prediction of time to metastasis from ocular melanoma fine needle aspirates , 2006 .
[8] Salim Roukos,et al. Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.
[9] Xiaochun Cao,et al. Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image , 2018, IEEE Transactions on Medical Imaging.
[10] T. Wong,et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. , 2014, Ophthalmology.
[11] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[12] Langis Gagnon,et al. Automatic visual quality assessment in optical fundus images , 2001 .
[13] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Aaron Y. Lee,et al. Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration , 2016, bioRxiv.
[15] Jianzhong Wu,et al. Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images , 2016, IEEE Transactions on Medical Imaging.
[16] Jost B. Jonas,et al. Updates on the Epidemiology of Age‐Related Macular Degeneration , 2017, Asia-Pacific journal of ophthalmology.
[17] Hamid Jafarkhani,et al. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI , 2015, Medical Image Anal..
[18] Yan Xu,et al. Deep learning of feature representation with multiple instance learning for medical image analysis , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[19] Jitendra Malik,et al. Cross Modal Distillation for Supervision Transfer , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Ronald M. Summers,et al. Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .
[21] O. Chapelle,et al. Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews] , 2009, IEEE Transactions on Neural Networks.
[22] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[23] Thomas Pock,et al. Learning a variational network for reconstruction of accelerated MRI data , 2017, Magnetic resonance in medicine.
[24] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[25] Quoc V. Le,et al. Self-Training With Noisy Student Improves ImageNet Classification , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Vijay S. Pande,et al. Massively Multitask Networks for Drug Discovery , 2015, ArXiv.
[27] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[28] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[29] Christopher Bowd,et al. Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers , 2014, PloS one.
[30] Rich Caruana,et al. Multitask Learning: A Knowledge-Based Source of Inductive Bias , 1993, ICML.
[31] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[32] Mauro Giacomini,et al. Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach , 2015, BMC Ophthalmology.
[33] Arun D. Singh,et al. Uveal melanoma: trends in incidence, treatment, and survival. , 2011, Ophthalmology.
[34] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[35] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[36] Manish Gupta,et al. Predicting Post-operative Visual Acuity for LASIK Surgeries , 2016, PAKDD.
[37] Leonardo Torquetti,et al. Predictors of Clinical Outcomes after Intrastromal Corneal Ring Segments Implantation , 2012 .
[38] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[39] Dinggang Shen,et al. 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients , 2016, MICCAI.
[40] Andrew Janowczyk,et al. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases , 2016, Journal of pathology informatics.