Firmenname: Julian Abhari
Über: Skin Check is a free assistive tool to allow anyone to check their moles for
skin cancer, track their moles over time, and compare their moles to an index of
different cancerous and non-cancerous mole types.
According to the Skin Cancer
Foundation, two people die every hour from skin-cancer in the U.
Nachfolgend finden Sie unsere wichtigsten Empfehlungen zur Kontaktaufnahme mit Skin-Check. Wir führen auf den direkten Seiten ihrer Website Vermutungen durch, um Hilfe bei Problemen wie der Verwendung ihrer Website / App, Abrechnungen, Preisen, Verwendung, Integrationen und anderen Problemen zu erhalten. Sie können eine der folgenden Methoden ausprobieren, um Skin-Check. Finden Sie heraus, mit welchen Optionen Ihre Kundendienstprobleme am schnellsten gelöst werden können..
Die folgenden Kontaktoptionen stehen zur Verfügung: Preisinformationen, Support, Allgemeine Hilfe und Presseinformationen/Berichterstattung (um die Reputation des Unternehmens zu bewerten)
NOTE: If the links below doesn't work for you, Please go directly to the Homepage of Julian Abhari
E-Mail: [email protected]
Website: 🌍 AI Dermatologist Website besuchen
Privatsphäre Politik: http://ai-derm.com/privacy/privacy.html
Developer: Acina
durch Keep It Kiss
Good example why research dataset trained models do not work on data from real devices because the models are not made to handle domain shift (lighting, camera, skin color), noise or counteract the dataset bias.
Without flash it predicts mole 90% confident.
With flash it predicts 90% cancer.
At least its good advertisement for dermatologists, mine certainly had a good laugh when he tried this.
At the very least, this needs an attribution (XAI) view so the user can turn of when the model detects hairs as features of a dark melanoma. The attention map should be post editable, or as skinvision does it, the problem are should be detected first.
If they ran XAI on this model before deployment, they would have realized this.
durch Keep It Kiss
Good example why research dataset trained models do not work on data from real devices because the models are not made to handle domain shift (lighting, camera, skin color), noise or counteract the dataset bias.
Without flash it predicts mole 90% confident.
With flash it predicts 90% cancer.
At least its good advertisement for dermatologists, mine certainly had a good laugh when he tried this.
At the very least, this needs an attribution (XAI) view so the user can turn of when the model detects hairs as features of a dark melanoma. The attention map should be post editable, or as skinvision does it, the problem are should be detected first.
If they ran XAI on this model before deployment, they would have realized this.
durch Keep It Kiss
Good example why research dataset trained models do not work on data from real devices because the models are not made to handle domain shift (lighting, camera, skin color), noise or counteract the dataset bias.
Without flash it predicts mole 90% confident.
With flash it predicts 90% cancer.
At least its good advertisement for dermatologists, mine certainly had a good laugh when he tried this.
At the very least, this needs an attribution (XAI) view so the user can turn of when the model detects hairs as features of a dark melanoma. The attention map should be post editable, or as skinvision does it, the problem are should be detected first.
If they ran XAI on this model before deployment, they would have realized this.
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