Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study

[1]  Ferdi van der Heijden,et al.  Diagnostic values for skin temperature assessment to detect diabetes-related foot complications. , 2014, Diabetes technology & therapeutics.

[2]  Zenghui Wang,et al.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.

[3]  R. Snyder,et al.  Diabetic foot ulcers--effects on QOL, costs, and mortality and the role of standard wound care and advanced-care therapies. , 2009, Ostomy/wound management.

[4]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[6]  Mohammed A. Fadhel,et al.  Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis , 2020, Electronics.

[7]  Neil D. Reeves,et al.  DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

[8]  Bengisu Tulu,et al.  Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification , 2017, IEEE Transactions on Biomedical Engineering.

[9]  Muhammad Naseer Bajwa,et al.  Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks , 2020, Applied Sciences.

[10]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[11]  Qiang Yu,et al.  Deep Convolutional Network Based on Pyramid Architecture , 2018, IEEE Access.

[12]  Héctor Mesa,et al.  Binary Tissue Classification on Wound Images With Neural Networks and Bayesian Classifiers , 2010, IEEE Transactions on Medical Imaging.

[13]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[14]  J. Shaw,et al.  Global estimates of the prevalence of diabetes for 2010 and 2030. , 2010, Diabetes research and clinical practice.

[15]  Omran Al-Shamma,et al.  DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network , 2019, Multimedia Tools and Applications.

[16]  Mohammed A. Fadhel,et al.  Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model , 2020, Electronics.

[17]  Boguslaw Cyganek,et al.  Image recognition with deep neural networks in presence of noise - Dealing with and taking advantage of distortions , 2017, Integr. Comput. Aided Eng..

[18]  B. Larijani,et al.  Assessment and treatment of diabetic foot ulcer , 2007, International journal of clinical practice.

[19]  Kevin Woo,et al.  Diabetic foot ulcers: Part II. Management. , 2014, Journal of the American Academy of Dermatology.

[20]  Ambady Ramachandran,et al.  Trends in prevalence of diabetes in Asian countries. , 2012, World journal of diabetes.

[21]  L. Yazdanpanah,et al.  Risk assessment of patients with diabetes for foot ulcers according to risk classification consensus of International Working Group on Diabetic Foot (IWGDF) , 2013, Pakistan journal of medical sciences.

[22]  Martin Lauer,et al.  3D Traffic Scene Understanding From Movable Platforms , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Georgeanne Botek,et al.  Treatment for diabetic foot ulcers , 2005, The Lancet.

[24]  M. Mohajeri-Tehrani,et al.  Nurses’ role in diabetic foot prevention and care; a review , 2012, Journal of Diabetes & Metabolic Disorders.

[25]  Oscar Camacho-Nieto,et al.  A Transfer Learning Method for Pneumonia Classification and Visualization , 2020, Applied Sciences.

[26]  A. Nather,et al.  Epidemiology of diabetic foot problems and predictive factors for limb loss. , 2008, Journal of diabetes and its complications.

[27]  Ming-Hsuan Yang,et al.  Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Xuelong Li,et al.  Transfer learning for pedestrian detection , 2013, Neurocomputing.

[29]  Diane J. Cook,et al.  Transfer learning for activity recognition: a survey , 2013, Knowledge and Information Systems.

[30]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[31]  Hazem Wannous,et al.  Enhanced Assessment of the Wound-Healing Process by Accurate Multiview Tissue Classification , 2011, IEEE Transactions on Medical Imaging.

[32]  Ferdi van der Heijden,et al.  Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis , 2015, Journal of biomedical optics.

[33]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.