Search Results for author: Harald Kittler

Found 6 papers, 3 papers with code

Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images

no code implementations14 Nov 2019 Susanne Kimeswenger, Elisabeth Rumetshofer, Markus Hofmarcher, Philipp Tschandl, Harald Kittler, Sepp Hochreiter, Wolfram Hötzenecker, Günter Klambauer

The aim of this study is to evaluate whether it is possible to detect basal cell carcinomas in histological sections using attention-based deep learning models and to overcome the ultra-high resolution and the weak labels of whole slide images.

whole slide images

Dermtrainer: A Decision Support System for Dermatological Diseases

no code implementations1 Jul 2019 Gernot Salzer, Agata Ciabattoni, Christian Fermüller, Martin Haiduk, Harald Kittler, Arno Lukas, Rosa María Rodríguez Domínguez, Antonia Wesinger, Elisabeth Riedl

Its key components are a comprehensive dermatological knowledge base, a clinical algorithm for diagnosing skin diseases, a reasoning component for deducing the most likely differential diagnoses for a patient, and a library of high-quality images.

Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

15 code implementations9 Feb 2019 Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, Allan Halpern

This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest public repository of dermoscopic images of skin.

Lesion Segmentation

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

11 code implementations28 Mar 2018 Philipp Tschandl, Cliff Rosendahl, Harald Kittler

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images.

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