Zobrazeno 1 - 10
of 405
pro vyhledávání: '"P, Tschandl"'
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:
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
Publikováno v:
JEADV Clinical Practice, Vol 3, Iss 2, Pp 785-788 (2024)
Externí odkaz:
https://doaj.org/article/a4bf2db753a548c5aa8e389a37544212
Autor:
Tirtha Chanda, Katja Hauser, Sarah Hobelsberger, Tabea-Clara Bucher, Carina Nogueira Garcia, Christoph Wies, Harald Kittler, Philipp Tschandl, Cristian Navarrete-Dechent, Sebastian Podlipnik, Emmanouil Chousakos, Iva Crnaric, Jovana Majstorovic, Linda Alhajwan, Tanya Foreman, Sandra Peternel, Sergei Sarap, İrem Özdemir, Raymond L. Barnhill, Mar Llamas-Velasco, Gabriela Poch, Sören Korsing, Wiebke Sondermann, Frank Friedrich Gellrich, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Matthias Goebeler, Bastian Schilling, Jochen S. Utikal, Kamran Ghoreschi, Stefan Fröhling, Eva Krieghoff-Henning, Reader Study Consortium, Titus J. Brinker
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-17 (2024)
Abstract Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet oft
Externí odkaz:
https://doaj.org/article/a7c398cf0b3b4b35ad5fc17b7f22b907
Autor:
Alessandra Cartocci, Alessio Luschi, Linda Tognetti, Elisa Cinotti, Francesca Farnetani, Aimilios Lallas, John Paoli, Caterina Longo, Elvira Moscarella, Danica Tiodorovic, Ignazio Stanganelli, Mariano Suppa, Emi Dika, Iris Zalaudek, Maria Antonietta Pizzichetta, Jean Luc Perrot, Gabriele Cevenini, Ernesto Iadanza, Giovanni Rubegni, Harald Kittler, Philipp Tschandl, Pietro Rubegni
Publikováno v:
Bioengineering, Vol 11, Iss 10, p 1036 (2024)
Diagnosing atypical pigmented facial lesions (aPFLs) is a challenging topic for dermatologists. Accurate diagnosis of these lesions is crucial for effective patient management, especially in dermatology, where visual assessment plays a central role.
Externí odkaz:
https://doaj.org/article/b86db7d416c5463e8a4ab2a3632002b2
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
Autor:
Scott W Menzies, ProfPhD, Christoph Sinz, MD, Michelle Menzies, BSc, Serigne N Lo, PhD, William Yolland, BSc, Johann Lingohr, BSc, Majid Razmara, PhD, Philipp Tschandl, PhD, Pascale Guitera, ProfPhD, Richard A Scolyer, ProfMD, Florentina Boltz, MD, Liliane Borik-Heil, MD, Hsien Herbert Chan, MD, David Chromy, MD, David J Coker, MD, Helena Collgros, MD, Maryam Eghtedari, MD, Marina Corral Forteza, MD, Emily Forward, MD, Bruna Gallo, MD, Stephanie Geisler, MD, Matthew Gibson, MMed, Amelie Hampel, MD, Genevieve Ho, MD, Laura Junez, MD, Philipp Kienzl, PhD, Arthur Martin, MD, Fergal J Moloney, MD, Amanda Regio Pereira, MD, Julia Maria Ressler, MD, Susanne Richter, MD, Katharina Silic, MD, Thomas Silly, MD, Michael Skoll, MD, Julia Tittes, MD, Philipp Weber, MD, Wolfgang Weninger, ProfPhD, Doris Weiss, MD, Ping Woo-Sampson, MD, Catherine Zilberg, MD, Harald Kittler, MD
Publikováno v:
The Lancet: Digital Health, Vol 5, Iss 10, Pp e679-e691 (2023)
Summary: Background: Diagnosis of skin cancer requires medical expertise, which is scarce. Mobile phone-powered artificial intelligence (AI) could aid diagnosis, but it is unclear how this technology performs in a clinical scenario. Our primary aim w
Externí odkaz:
https://doaj.org/article/bb536ef03af64214ad529982414e2889