Dermatologist-level classification of skin cancer with deep neural networks

By Andre Esteva, Brett Kuprel, R. Novoa, J. Ko, S. Swetter, Helen M. Blau, Sebastian Thrun. 2017

12,830 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Epidemiology, Oncology

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Citations in the corpus

Extracting Training Data from Large Language Models (2020, background): “Our attacks directly apply to any language model, including those trained on sensitive and non-public data [8, 14].”
Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, background): “Deep learning based image classification models have also been explored for applications in dermatology (Esteva et al., 2017).”
Model-Reuse Attacks on Deep Learning Systems (2018, methodology): “In [20], using a pre-trained Inception.”
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge (2019, background): “In recent years, deep learning has achieved impressive results in diagnosing skin cancer, with predictive accuracy sometimes comparable to human doctors (Esteva et al., 2017).”
Explainable AI: The New 42? (2018, background): “Deep learning approaches, including the more sophisticated reinforcement learning architectures, exceed human performance in many areas [6,17,18,24].”
What do we need to build explainable AI systems for the medical domain? (2017, background): “Deep neural networks (DNN), particularly convolutional neural networks (CNN) and recurrent neural networks (RNN) have demonstrated to be applicable to a wide range of practical problems, from image recognition (Simonyan and Zisserman, 2014) and image classification (Esteva et al., 2017) to movement ”
An overview of deep learning in medical imaging focusing on MRI (2018, background): “classifying skin lesions according to malignancy [36] or discovering”
Recognition in Terra Incognita (2018, methodology): “Automated visual recognition algorithms have recently achieved human expert performance at visual classification tasks in field biology [1–3] and medicine [4, 5].”
Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “also attempted to classify skin diseases using AI by analyzing a database of about 130,000 images and their corresponding 2000 diseases, using one of the deep learning algorithms, convolutional neural network [65].”
Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017, methodology): “the tasks of medical image processing [1], playing games such as Go and Atari [2], [3], etc.”
Opportunities and obstacles for deep learning in biology and medicine (2017, background): “More recently, deep learning algorithms have shown promise in fields as diverse as highenergy physics [3], dermatology [4], and translation among written languages [5].”
NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, background): “Since then, deep learning techniques have affected the application of machine learning in biology expanding the ability of prediction tools to produce more accurate results on complex datasets (11-18).”
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations (2017, background): “In recent years, deep neural networks (DNNs) have led to many breakthrough results in machine learning and computer vision [20, 28, 10], and are now widely deployed in industry.”
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift (2019, methodology): “aided medical diagnoses from imaging (Esteva et al., 2017) to self-driving cars (Bojarski et al.”