Robustness of convolutional neural networks in recognition of pigmented skin lesions.
暂无分享,去创建一个
Jakob Nikolas Kather | R. Maron | J. Utikal | A. Hekler | A. Hauschild | S. Haferkamp | D. Schadendorf | B. Schilling | C. von Kalle | S. Fröhling | T. Brinker | W. Sondermann | M. Heppt | L. French | D. Lipka | H. Kutzner | K. Ghoreschi | M. Schlaak | F. Meier | E. Krieghoff-Henning | F. Gellrich | Sarah Haggenmüller
[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).