Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study

Journals

1. Haggenmüller S, Maron R, Hekler A, Utikal J, Barata C, Barnhill R, Beltraminelli H, Berking C, Betz-Stablein B, Blum A, Braun S, Carr R, Combalia M, Fernandez-Figueras M, Ferrara G, Fraitag S, French L, Gellrich F, Ghoreschi K, Goebeler M, Guitera P, Haenssle H, Haferkamp S, Heinzerling L, Heppt M, Hilke F, Hobelsberger S, Krahl D, Kutzner H, Lallas A, Liopyris K, Llamas-Velasco M, Malvehy J, Meier F, Müller C, Navarini A, Navarrete-Dechent C, Perasole A, Poch G, Podlipnik S, Requena L, Rotemberg V, Saggini A, Sangueza O, Santonja C, Schadendorf D, Schilling B, Schlaak M, Schlager J, Sergon M, Sondermann W, Soyer H, Starz H, Stolz W, Vale E, Weyers W, Zink A, Krieghoff-Henning E, Kather J, von Kalle C, Lipka D, Fröhling S, Hauschild A, Kittler H, Brinker T. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. European Journal of Cancer 2021;156:202 View
2. Nie Y, Sommella P, Carratu M, Ferro M, O'Nils M, Lundgren J. Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning. IEEE Access 2022;10:95716 View
3. Liopyris K, Gregoriou S, Dias J, Stratigos A. Artificial Intelligence in Dermatology: Challenges and Perspectives. Dermatology and Therapy 2022;12(12):2637 View
4. Rezk E, Eltorki M, El-Dakhakhni W. Leveraging Artificial Intelligence to Improve the Diversity of Dermatological Skin Color Pathology: Protocol for an Algorithm Development and Validation Study. JMIR Research Protocols 2022;11(3):e34896 View
5. Cerminara S, Cheng P, Kostner L, Huber S, Kunz M, Maul J, Böhm J, Dettwiler C, Geser A, Jakopović C, Stoffel L, Peter J, Levesque M, Navarini A, Maul L. Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening?. European Journal of Cancer 2023;190:112954 View
6. Li X, Zhao X, Ma H, Xie B. Image Analysis and Diagnosis of Skin Diseases - A Review. Current Medical Imaging Formerly Current Medical Imaging Reviews 2023;19(3):199 View
7. Mirikharaji Z, Abhishek K, Bissoto A, Barata C, Avila S, Valle E, Celebi M, Hamarneh G. A survey on deep learning for skin lesion segmentation. Medical Image Analysis 2023;88:102863 View
8. Hartmann T, Passauer J, Hartmann J, Schmidberger L, Kneilling M, Volc S. Basic principles of artificial intelligence in dermatology explained using melanoma. JDDG: Journal der Deutschen Dermatologischen Gesellschaft 2024;22(3):339 View
9. Hartmann T, Passauer J, Hartmann J, Schmidberger L, Kneilling M, Volc S. Grundprinzipien der künstlichen Intelligenz in der Dermatologie erklärt am Beispiel des Melanoms. JDDG: Journal der Deutschen Dermatologischen Gesellschaft 2024;22(3):339 View
10. Naeem A, Anees T, Mahmood T. DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images. PLOS ONE 2024;19(3):e0297667 View
11. Malaiarasan S, Ravi R. Integrating Multi‐Scale Feature Boundary Module and Feature Fusion With CNN for Accurate Skin Cancer Segmentation and Classification. International Journal of Imaging Systems and Technology 2024;34(5) View

Books/Policy Documents

1. Jaworek-Korjakowska J, Wojcicka A, Kucharski D, Brodzicki A, Kendrick C, Cassidy B, Yap M. Computer Vision – ECCV 2022 Workshops. View
2. Yen A, Wu C, Chen H. Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases. View
3. Du S, Bayasi N, Hamarneh G, Garbi R. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops. View