Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta‐analysis of studies performed in a clinical setting

By M. Vestergaard, Petra Macaskill, P. E. Holt, S. Menzies. British Journal of Dermatology, 2008

861 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Oncology, Ophthalmology

View Paper: https://www.semanticscholar.org/paper/0a908402177ee2c2dc7120f8728f4fa7eceb12f8

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In Oncology

1. Adel Al-Jumaily
2. Ammara Masood
3. Yading Yuan
4. Yeh-Chi Lo
5. M. Chao

Citations in the corpus

• Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists (2018, background): “Several meta-analyses have shown that dermoscopy significantly improves the diagnostic accuracy of the naked eye examination [3–5].”
• Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms (2013, background): “Dermoscopy enables better diagnosis as compared to unaided eye [14–16] with an improvement in diagnostic sensitivity of 10–30% [17].”
• Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance (2017, background): “Although dermoscopy has been shown to lead to increased diagnostic accuracy comparing to the conventional ABCD criteria [4], proper interpretation of dermoscopic images is normally time-consuming, complex, and prone to suffer from inter- and intra-observer variabilities.”
• Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval (2021, background): “incident light and oil/gel immersion to make skin subsurface structures, dermoepidermal junction, and upper dermis accessible to visual examination [8], [9], [10].”