Zobrazeno 1 - 10
of 308
pro vyhledávání: '"TSCHANDL, Philipp"'
Autor:
Yan, Siyuan, Yu, Zhen, Primiero, Clare, Vico-Alonso, Cristina, Wang, Zhonghua, Yang, Litao, Tschandl, Philipp, Hu, Ming, Tan, Gin, Tang, Vincent, Ng, Aik Beng, Powell, David, Bonnington, Paul, See, Simon, Janda, Monika, Mar, Victoria, Kittler, Harald, Soyer, H. Peter, Ge, Zongyuan
Diagnosing and treating skin diseases require advanced visual skills across multiple domains and the ability to synthesize information from various imaging modalities. Current deep learning models, while effective at specific tasks such as diagnosing
Externí odkaz:
http://arxiv.org/abs/2410.15038
Autor:
Chanda, Tirtha, Haggenmueller, Sarah, Bucher, Tabea-Clara, Holland-Letz, Tim, Kittler, Harald, Tschandl, Philipp, Heppt, Markus V., Berking, Carola, Utikal, Jochen S., Schilling, Bastian, Buerger, Claudia, Navarrete-Dechent, Cristian, Goebeler, Matthias, Kather, Jakob Nikolas, Schneider, Carolin V., Durani, Benjamin, Durani, Hendrike, Jansen, Martin, Wacker, Juliane, Wacker, Joerg, Consortium, Reader Study, Brinker, Titus J.
Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' confidence and trust in AI-driven decisions. Despite these advancements
Externí odkaz:
http://arxiv.org/abs/2409.13476
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 1, p e15597 (2020)
BackgroundThe diagnosis of pigmented skin lesion is error prone and requires domain-specific expertise, which is not readily available in many parts of the world. Collective intelligence could potentially decrease the error rates of nonexperts. Obje
Externí odkaz:
https://doaj.org/article/4e1a6894a1bc46d288ef5b8babf601f0
Publikováno v:
J Eur Acad Dermatol Venereol. 2023 May 31. Epub ahead of print
Background: As available medical image datasets increase in size, it becomes infeasible for clinicians to review content manually for knowledge extraction. The objective of this study was to create an automated clustering resulting in human-interpret
Externí odkaz:
http://arxiv.org/abs/2309.08533
Autor:
Chanda, Tirtha, Hauser, Katja, Hobelsberger, Sarah, Bucher, Tabea-Clara, Garcia, Carina Nogueira, Wies, Christoph, Kittler, Harald, Tschandl, Philipp, Navarrete-Dechent, Cristian, Podlipnik, Sebastian, Chousakos, Emmanouil, Crnaric, Iva, Majstorovic, Jovana, Alhajwan, Linda, Foreman, Tanya, Peternel, Sandra, Sarap, Sergei, Özdemir, İrem, Barnhill, Raymond L., Velasco, Mar Llamas, Poch, Gabriela, Korsing, Sören, Sondermann, Wiebke, Gellrich, Frank Friedrich, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Drexler, Konstantin, Goebeler, Matthias, Schilling, Bastian, Utikal, Jochen S., Ghoreschi, Kamran, Fröhling, Stefan, Krieghoff-Henning, Eva, Brinker, Titus J.
Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma poses severe obstacles to user acceptance. Explainable artificial int
Externí odkaz:
http://arxiv.org/abs/2303.12806
Autor:
Liopyris, Konstantinos, Navarrete-Dechent, Cristian, Marchetti, Michael A., Rotemberg, Veronica, Apalla, Zoe, Argenziano, Giuseppe, Blum, Andreas, Braun, Ralph P., Carrera, Cristina, Codella, Noel C.F., Combalia, Marc, Dusza, Stephen W., Gutman, David A., Helba, Brian, Hofmann-Wellenhof, Rainer, Jaimes, Natalia, Kittler, Harald, Kose, Kivanc, Lallas, Aimilios, Longo, Caterina, Malvehy, Josep, Menzies, Scott, Nelson, Kelly C., Paoli, John, Puig, Susana, Rabinovitz, Harold S., Rishpon, Ayelet, Russo, Teresa, Scope, Alon, Soyer, H. Peter, Stein, Jennifer A., Stolz, Willhelm, Sgouros, Dimitrios, Stratigos, Alexander J., Swanson, David L., Thomas, Luc, Tschandl, Philipp, Zalaudek, Iris, Weber, Jochen, Halpern, Allan C., Marghoob, Ashfaq A.
Publikováno v:
In Journal of Investigative Dermatology March 2024 144(3):531-539
Autor:
Unterluggauer, Luisa1 (AUTHOR) luisa.unterluggauer@meduniwien.ac.at, Tschandl, Philipp1 (AUTHOR), Harrison, Nicole2 (AUTHOR), Borik‐Heil, Liliane1 (AUTHOR)
Publikováno v:
JEADV Clinical Practice. Jun2024, Vol. 3 Issue 2, p785-788. 4p.
Malignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin lesions. Among different computer-based methods, deep learning-based ap
Externí odkaz:
http://arxiv.org/abs/2008.12602
Autor:
Rotemberg, Veronica, Kurtansky, Nicholas, Betz-Stablein, Brigid, Caffery, Liam, Chousakos, Emmanouil, Codella, Noel, Combalia, Marc, Dusza, Stephen, Guitera, Pascale, Gutman, David, Halpern, Allan, Kittler, Harald, Kose, Kivanc, Langer, Steve, Lioprys, Konstantinos, Malvehy, Josep, Musthaq, Shenara, Nanda, Jabpani, Reiter, Ofer, Shih, George, Stratigos, Alexander, Tschandl, Philipp, Weber, Jochen, Soyer, H. Peter
Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies ex
Externí odkaz:
http://arxiv.org/abs/2008.07360
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