Diagnostic accuracy of dermoscopy.

By Harald Kittler, H. Pehamberger, K. Wolff, Michael Binder. The Lancet Oncology, 2002

1,290 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Immunology and Allergy, Oncology

View Paper: https://www.semanticscholar.org/paper/adbc099706271bd1e04f0e14506e02cbeb13a44c

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In Biomedical Engineering

In Oncology

Citations in the corpus

Cutaneous melanoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. (2019, background): “Dermoscopy by an experienced physician enhances the diagnostic accuracy [II, B] [7].”
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification (2019, background): “Dermoscopy is one of the essential means to improve the diagnostic performance and reduce skin cancer deaths [3].”
Deep learning ensembles for melanoma recognition in dermoscopy images (2016, background): “However, recognition performance drops significantly when the clinicians are not adequately trained [8, 9].”
BCN20000: Dermoscopic Lesions in the Wild (2019, methodology): “Prior research has found that this technique permits improved visualization of the lesion structures, enhancing the accuracy of dermatologists [1, 9].”
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, background): “Even though most melanomas are first discovered by patients [4], the diagnostic accuracy of unaided expert visual inspection is only about 60% [5].”
Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval (2021, background): “diagnosis accuracy achieved with the unaided eye is slightly better than 60% [6], [7], making the early detection very hard to obtain.”
Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets (2019, background): “[4] reported that, for experienced dermatologists, the accuracy in diagnosing pigmented skin lesions improves when using”
Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks (2017, background): “Dermoscopy is a non-invasive dermatology imaging technique for the in vivo observation of pigmented skin lesions [3] and plays an important role in the early diagnosis of malignant melanoma [2].”
Skin lesion classification from dermoscopic images using deep learning techniques (2017, background): “Use of dermoscopic images improves diagnostic accuracy of skin lesions by 49% [4].”
PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones (2020, background): “Nonetheless, accurate diagnosis is challenging and requires proper training and experience in dermoscopy 4–6 , a noninvasive diagnostic technique that uses optic magniﬁcation to permit the visualization of morphologic features that are not visible to the naked eye 7 .”
Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC) (2016, background): “3) Disease recognition performance is within the range of that reported in prior literature for expert dermatologists [5].”
Attention Residual Learning for Skin Lesion Classification (2019, background): “Dermoscopy [3], [4], a recent technique of visual inspection that both magnifies the skin and eliminates surface reflection, is one of the”