Superior skin cancer classification by the combination of human and artificial intelligence

Hekler, Achim; Hauschild, Axel; Weichenthal, Michael; Maron, Roman C.; Berking, Carola; Haferkamp, Sebastian; Klode, Joachim; Schadendorf, Dirk; Schilling, Bastian; Holland‐Letz, Tim; Izar, Benjamin; Kalle, Christof von; Fröhling, Stefan; Brinker, Titus J.; Schmitt, Laurenz; Peitsch, Wiebke K.; Hoffmann, Friederike; Becker, Jürgen C.; Drusio, Christina; Jansen, Philipp; Lodde, Georg; Sammet, Stefanie; Sondermann, Wiebke; Ugurel, Selma; Zader, Jeannine; Enk, Alexander; Salzmann, Martin; Schäfer, Sarah; Schäkel, Knut; Winkler, Julia; Wölbing, Priscilla; Asper, Hiba; Bohne, Ann Sophie; Brown, Victoria; Burba, Bianca; Deffaa, Sophia; Dietrich, Cecilia; Dietrich, Matthias; Drerup, Katharina; Egberts, Friederike; Erkens, Anna Sophie; Greven, Salim; Harde, Viola; Jost, Marion; Kaeding, Merit; Kosova, Katharina; Lischner, Stephan; Maagk, Maria; Messinger, Anna Laetitia; Metzner, Malte; Motamedi, Rogina; Rosenthal, Ann Christine; Seidl, Ulrich; Stemmermann, Jana; Torz, Kaspar; Velez, Juliana Giraldo; Haiduk, Jennifer; Alter, Mareike; Bär, Claudia; Bergenthal, Paul; Gerlach, Anne; Holtorf, Christian; Karoglan, Ante; Kindermann, Sophie; Kraas, Luise; Felcht, Moritz; Gaiser, Maria Rita; Klemke, Claus D.; Kurzen, Hjalmar; Leibing, Thomas; Müller, Verena; Reinhard, Raphael; Utikal, Jochen; Winter, Franziska; Eicher, Laurie; Hartmann, Daniela; Heppt, Markus V.; Kilian, Katharina; Krammer, Sebastian; Lill, Diana; Niesert, Anne Charlotte; Oppel, Eva; Sattler, Elke; Senner, Sonja; Wallmichrath, Jens; Wolff, Hans; Gesierich, Anja; Giner, Tina; Glutsch, Valerie; Kerstan, Andreas; Presser, Dagmar; Schrüfer, Philipp; Schummer, Patrick; Stolze, Ina; Weber, Judith; Drexler, Konstantin; Mickler, Marion; Stauner, Camila Toledo; Thiem, Alexander

doi:10.1016/j.ejca.2019.07.019

Cited by 222 publications

(98 citation statements)

References 20 publications

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“…Nevertheless, further validation in prospective clinical trials in more real-world settings is necessary before claiming superiority of algorithm performance over dermatologists. While most of these studies pitted artificial intelligence algorithms against dermatologists, a recent study by Hekler et al [43] found that combining human and artificial intelligence accomplishes a better classification of images as compared to only dermatologists or only classification by a CNN [43]. The mean accuracy increased by 1.36% when dermatologists worked together with ML.…”

Section: Melanomamentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

147

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Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

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Section: Melanomamentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

147

View full text Add to dashboard Cite

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“…[ 6 ] and Hekler et al . [ 14 ] to develop classification models to aid physicians in the diagnosis of skin cancer, skin lesions, and psoriasis. In particular, Esteva et al .,[ 6 ] trained a deep convolutional neural network (DCNN) model using 129,450 images to classify images into one of two categories (also known as binary classification problem in machine learning) as either keratinocyte carcinoma or seborrheic keratosis; and malignant melanoma or benign nevus.…”

Section: History Of Ai In Medical Fieldmentioning

confidence: 99%

Artificial intelligence: How is it changing medical sciences and its future?

Basu¹,

Sinha

Ong³

et al. 2020

Indian J Dermatol

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Artificially intelligent computer systems are used extensively in medical sciences. Common applications include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical documents, such as prescriptions, and remotely treating patients. While computer systems often execute tasks more efficiently than humans, more recently, state-of-the-art computer algorithms have achieved accuracies which are at par with human experts in the field of medical sciences. Some speculate that it is only a matter of time before humans are completely replaced in certain roles within the medical sciences. The motivation of this article is to discuss the ways in which artificial intelligence is changing the landscape of medical science and to separate hype from reality.

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“…Hekler et al [17] combined human and artificial intelligence for classifying skin cancer. A single CNN was trained using 11,444 dermoscopic images to classify skin lesion images into five classes.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobile-based Skin Lesions Classification Using Convolution Neural Network

Hameed¹,

Shabut²,

Hameed³

et al. 2020

AETiC

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This research work is aimed at investing skin lesions classification problem using Convolution Neural Network (CNN) using cloud-server architecture. Using the cloud services and CNN, a real-time mobile-enabled skin lesions classification expert system “i-Rash” is proposed and developed. i-Rash aimed at early diagnosis of acne, eczema and psoriasis at remote locations. The classification model used in the “i-Rash” is developed using the CNN model “SqueezeNet”. The transfer learning approach is used for training the classification model and model is trained and tested on 1856 images. The benefit of using SqueezeNet results in a limited size of the trained model i.e. only 3 MB. For classifying new image, cloud-based architecture is used, and the trained model is deployed on a server. A new image is classified in fractions of seconds with overall accuracy, sensitivity and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash can serve in initial classification of skin lesions, hence, can play a very important role early classification of skin lesions for people living in remote areas.

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Superior skin cancer classification by the combination of human and artificial intelligence

Cited by 222 publications

References 20 publications

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Artificial intelligence: How is it changing medical sciences and its future?

Mobile-based Skin Lesions Classification Using Convolution Neural Network

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