000145637 001__ 145637
000145637 005__ 20200925153944.0
000145637 037__ $$aDZNE-2020-00967
000145637 041__ $$aEnglish
000145637 1001_ $$0P:(DE-2719)2812477$$aConjeti, Sailesh$$b0$$udzne
000145637 1112_ $$aMICCAI 2018$$cGranada$$d2018-09-16 - 2018-09-16$$wSpain
000145637 245__ $$aWebly Supervised Learning for Skin Lesion Classification
000145637 260__ $$c2018
000145637 3367_ $$033$$2EndNote$$aConference Paper
000145637 3367_ $$2DataCite$$aOther
000145637 3367_ $$2BibTeX$$aINPROCEEDINGS
000145637 3367_ $$2DRIVER$$aconferenceObject
000145637 3367_ $$2ORCID$$aLECTURE_SPEECH
000145637 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1597406579_15933$$xOther
000145637 520__ $$aWithin medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive. To improve the representativeness of the training dataset, for the first time, we present an approach to utilize large amounts of freely available web data through web-crawling. To handle noise and weak nature of web annotations, we propose a two-step transfer learning based training process with a robust loss function, termed as Webly Supervised Learning (WSL) to train deep models for the task. We also leverage search by image to improve the search specificity of our web-crawling and reduce cross-domain noise. Within WSL, we explicitly model the noise structure between classes and incorporate it to selectively distill knowledge from the web data during model training. To demonstrate improved performance due to WSL, we benchmarked on a publicly available 10-class fine-grained skin lesion classification dataset and report a significant improvement of top-1 classification accuracy from 71.25% to 80.53% due to the incorporation of web-supervision.
000145637 536__ $$0G:(DE-HGF)POF3-345$$a345 - Population Studies and Genetics (POF3-345)$$cPOF3-345$$fPOF III$$x0
000145637 8564_ $$uhttps://link.springer.com/chapter/10.1007/978-3-030-00934-2_45
000145637 909CO $$ooai:pub.dzne.de:145637$$pVDB
000145637 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812477$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b0$$kDZNE
000145637 9131_ $$0G:(DE-HGF)POF3-345$$1G:(DE-HGF)POF3-340$$2G:(DE-HGF)POF3-300$$aDE-HGF$$bForschungsbereich Gesundheit$$lErkrankungen des Nervensystems$$vPopulation Studies and Genetics$$x0
000145637 9141_ $$y2018
000145637 9201_ $$0I:(DE-2719)1040310$$kAG Reuter$$lImage Analysis$$x0
000145637 980__ $$aconf
000145637 980__ $$aVDB
000145637 980__ $$aI:(DE-2719)1040310
000145637 980__ $$aUNRESTRICTED