Robustness of convolutional neural networks in recognition of pigmented skin lesions.

[1]  Jakob Nikolas Kather,et al.  Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study , 2021, Journal of medical Internet research.

[2]  Roman C. Maron,et al.  Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study , 2020, Journal of medical Internet research.

[3]  John Paoli,et al.  Human–computer collaboration for skin cancer recognition , 2020, Nature Medicine.

[4]  H. Haenssle,et al.  Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions. , 2020, European journal of cancer.

[5]  Yongxia Zhou,et al.  Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet) , 2020, PloS one.

[6]  Jeremy Howard,et al.  fastai: A Layered API for Deep Learning , 2020, Inf..

[7]  Seung Seog Han,et al.  Augment Intelligence Dermatology : Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. , 2020, The Journal of investigative dermatology.

[8]  Jungkyu Lee,et al.  Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network , 2020, ArXiv.

[9]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[10]  Jakob Nikolas Kather,et al.  Pan-cancer image-based detection of clinically actionable genetic alterations , 2019, Nature Cancer.

[11]  Douglas Heaven,et al.  Why deep-learning AIs are so easy to fool , 2019, Nature.

[12]  Tim Holland-Letz,et al.  Superior skin cancer classification by the combination of human and artificial intelligence. , 2019, European journal of cancer.

[13]  Carola Berking,et al.  Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data. , 2019, European journal of cancer.

[14]  A. Enk,et al.  Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. , 2019, European journal of cancer.

[15]  A. Enk,et al.  Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. , 2019, European journal of cancer.

[16]  Tim Holland-Letz,et al.  Deep neural networks are superior to dermatologists in melanoma image classification. , 2019, European journal of cancer.

[17]  R. Hofmann-Wellenhof,et al.  Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition. , 2019, JAMA dermatology.

[18]  Verónica Vilaplana,et al.  BCN20000: Dermoscopic Lesions in the Wild , 2019, ArXiv.

[19]  Pedro A. Amado Assunção,et al.  Light Field Image Dataset of Skin Lesions , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  R. Hofmann-Wellenhof,et al.  Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. , 2019, The Lancet. Oncology.

[21]  Tim Holland-Letz,et al.  Pathologist-level classification of histopathological melanoma images with deep neural networks. , 2019, European journal of cancer.

[22]  Ekin D. Cubuk,et al.  Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation , 2019, ArXiv.

[23]  Achim Hekler,et al.  Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. , 2019, European journal of cancer.

[24]  Andrew L. Beam,et al.  Adversarial attacks on medical machine learning , 2019, Science.

[25]  Achim Hekler,et al.  A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. , 2019, European journal of cancer.

[26]  Alexander Binder,et al.  Unmasking Clever Hans predictors and assessing what machines really learn , 2019, Nature Communications.

[27]  Achim Hekler,et al.  Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark. , 2019, European journal of cancer.

[28]  M. Gurcan,et al.  Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study. , 2019, The Lancet. Oncology.

[29]  Constantino Carlos Reyes-Aldasoro,et al.  Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study , 2019, PLoS medicine.

[30]  Zhitao Gong,et al.  Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Issam El Naqa,et al.  Machine Learning and Imaging Informatics in Oncology , 2018, Oncology.

[32]  George E. Dahl,et al.  Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. , 2018, Archives of pathology & laboratory medicine.

[33]  H. Haenssle,et al.  Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[34]  Marcus A. Badgeley,et al.  Confounding variables can degrade generalization performance of radiological deep learning models , 2018, ArXiv.

[35]  S. Han,et al.  Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. , 2018, The Journal of investigative dermatology.

[36]  Yair Weiss,et al.  Why do deep convolutional networks generalize so poorly to small image transformations? , 2018, J. Mach. Learn. Res..

[37]  Harald Kittler,et al.  Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .

[38]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[39]  Aleksander Madry,et al.  Exploring the Landscape of Spatial Robustness , 2017, ICML.

[40]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[41]  David Dagan Feng,et al.  Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks , 2017, ArXiv.

[42]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[43]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[44]  Noel C. F. Codella,et al.  Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[45]  Pascal Frossard,et al.  Manitest: Are classifiers really invariant? , 2015, BMVC.

[46]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).