000145640 001__ 145640
000145640 005__ 20200925153944.0
000145640 037__ $$aDZNE-2020-00970
000145640 041__ $$aEnglish
000145640 1001_ $$0P:(DE-2719)2812477$$aConjeti, Sailesh$$b0$$udzne
000145640 1112_ $$aMICCAI 2018$$cGranada$$d2018-09-16 - 2018-09-16$$wSpain
000145640 245__ $$aGeneralizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples
000145640 260__ $$c2018
000145640 3367_ $$033$$2EndNote$$aConference Paper
000145640 3367_ $$2DataCite$$aOther
000145640 3367_ $$2BibTeX$$aINPROCEEDINGS
000145640 3367_ $$2DRIVER$$aconferenceObject
000145640 3367_ $$2ORCID$$aLECTURE_SPEECH
000145640 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1597406890_15935$$xOther
000145640 520__ $$aIn this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data. To this end, we utilize adversarial examples, images that fool machine learning models, while looking imperceptibly different from original data, as a measure to evaluate the robustness of a variety of medical imaging models. Through extensive experiments on skin lesion classification and whole brain segmentation with state-of-the-art networks such as Inception and UNet, we show that models that achieve comparable performance regarding generalizability may have significant variations in their perception of the underlying data manifold, leading to an extensive performance gap in their robustness.
000145640 536__ $$0G:(DE-HGF)POF3-345$$a345 - Population Studies and Genetics (POF3-345)$$cPOF3-345$$fPOF III$$x0
000145640 8564_ $$uhttps://link.springer.com/chapter/10.1007/978-3-030-00928-1_56
000145640 909CO $$ooai:pub.dzne.de:145640$$pVDB
000145640 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812477$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b0$$kDZNE
000145640 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
000145640 9141_ $$y2018
000145640 9201_ $$0I:(DE-2719)1040310$$kAG Reuter$$lImage Analysis$$x0
000145640 980__ $$aconf
000145640 980__ $$aVDB
000145640 980__ $$aI:(DE-2719)1040310
000145640 980__ $$aUNRESTRICTED