Search Results for author: Philipp Tschandl

Found 6 papers, 3 papers with code

The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification

1 code implementation28 Aug 2020 Amirreza Mahbod, Philipp Tschandl, Georg Langs, Rupert Ecker, Isabella Ellinger

In this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification.

Classification General Classification +3

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

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

Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features

no code implementations22 Oct 2018 Philipp Tschandl, Giuseppe Argenziano, Majid Razmara, Jordan Yap

Methods: A neural network was trained to predict disease classes on dermatoscopic images from three retrospectively collected image datasets containing 888, 2750 and 16691 images respectively.

Content-Based Image Retrieval General Classification

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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