Zobrazeno 1 - 10
of 750
pro vyhledávání: '"Navarini, Alexander"'
Autor:
Gröger, Fabian, Gottfrois, Philippe, Amruthalingam, Ludovic, Gonzalez-Jimenez, Alvaro, Lionetti, Simone, Soenksen-Martinez, Luis R., Navarini, Alexander A., Pouly, Marc
The growing demand for accurate and equitable AI models in digital dermatology faces a significant challenge: the lack of diverse, high-quality labeled data. In this work, we investigate the potential of domain-specific foundation models for dermatol
Externí odkaz:
http://arxiv.org/abs/2411.05514
Autor:
Gottfrois, Philippe, Gröger, Fabian, Andriambololoniaina, Faly Herizo, Amruthalingam, Ludovic, Gonzalez-Jimenez, Alvaro, Hsu, Christophe, Kessy, Agnes, Lionetti, Simone, Mavura, Daudi, Ng'ambi, Wingston, Ngongonda, Dingase Faith, Pouly, Marc, Rakotoarisaona, Mendrika Fifaliana, Rabenja, Fahafahantsoa Rapelanoro, Traoré, Ibrahima, Navarini, Alexander A.
Publikováno v:
Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024, MICCAI 2024, Lecture Notes in Computer Science, vol. 15003, Springer, Cham
Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatric population suffering from largely untreated skin conditions. The int
Externí odkaz:
http://arxiv.org/abs/2411.04584
Autor:
Gonzalez-Jimenez, Alvaro, Lionetti, Simone, Bazazian, Dena, Gottfrois, Philippe, Gröger, Fabian, Pouly, Marc, Navarini, Alexander
Out-Of-Distribution (OOD) detection is critical to deploy deep learning models in safety-critical applications. However, the inherent hierarchical concept structure of visual data, which is instrumental to OOD detection, is often poorly captured by c
Externí odkaz:
http://arxiv.org/abs/2403.15260
Autor:
Gröger, Fabian, Lionetti, Simone, Gottfrois, Philippe, Gonzalez-Jimenez, Alvaro, Groh, Matthew, Daneshjou, Roxana, Consortium, Labelling, Navarini, Alexander A., Pouly, Marc
Publikováno v:
Proceedings of the 3rd Machine Learning for Health Symposium, PMLR 225:101-128, 2023
Benchmark datasets for digital dermatology unwittingly contain inaccuracies that reduce trust in model performance estimates. We propose a resource-efficient data-cleaning protocol to identify issues that escaped previous curation. The protocol lever
Externí odkaz:
http://arxiv.org/abs/2309.06961
Autor:
Gonzalez-Jimenez, Alvaro, Lionetti, Simone, Gottfrois, Philippe, Gröger, Fabian, Pouly, Marc, Navarini, Alexander
This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data by controlling its sen
Externí odkaz:
http://arxiv.org/abs/2306.00753
Autor:
Gröger, Fabian, Lionetti, Simone, Gottfrois, Philippe, Gonzalez-Jimenez, Alvaro, Amruthalingam, Ludovic, Consortium, Labelling, Groh, Matthew, Navarini, Alexander A., Pouly, Marc
Benchmark datasets in computer vision often contain off-topic images, near duplicates, and label errors, leading to inaccurate estimates of model performance. In this paper, we revisit the task of data cleaning and formalize it as either a ranking pr
Externí odkaz:
http://arxiv.org/abs/2305.17048
Publikováno v:
In Journal of the American Academy of Dermatology October 2024 91(4):699-705
Autor:
Winkler, Julia K., Kommoss, Katharina S., Toberer, Ferdinand, Enk, Alexander, Maul, Lara V., Navarini, Alexander A., Hudson, Jeremy, Salerni, Gabriel, Rosenberger, Albert, Haenssle, Holger A.
Publikováno v:
In European Journal of Cancer May 2024 202
Autor:
Gössinger, Elisabeth, Dodiuk-Gad, Roni, Mühleisen, Beda, Oon, Hazel H., Oh, Choon Chiat, Maul, Julia-Tatjana, Navarini, Alexander A.
Publikováno v:
In Dermatologic Clinics April 2024 42(2):317-328
Publikováno v:
In Learning and Instruction December 2023 88