{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T14:42:54Z","timestamp":1769006574530,"version":"3.49.0"},"reference-count":38,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,4]]},"DOI":"10.23919\/fruct.2019.8711974","type":"proceedings-article","created":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T23:07:46Z","timestamp":1557788866000},"page":"472-478","source":"Crossref","is-referenced-by-count":18,"title":["Impact of Adversarial Examples on the Efficiency of Interpretation and Use of Information from High-Tech Medical Images"],"prefix":"10.23919","author":[{"given":"Aleksandra","family":"Vatian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalia","family":"Gusarova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalia","family":"Dobrenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergey","family":"Dudorov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niyaz","family":"Nigmatullin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anatoly","family":"Shalyto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Artem","family":"Lobantsev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref38","year":"0","journal-title":"The solution of team ‘grt123’"},{"key":"ref33","first-page":"91","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref32","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"ref31","article-title":"Evaluate the malignancy of pulmonary nodules using the 3D deep leaky noisy-or network","volume":"14","author":"liao","year":"2015","journal-title":"Journal of latex class files"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-017-0331-1"},{"key":"ref37","year":"0","journal-title":"The DICOM Standard Medical Imaging & Technology Alliance"},{"key":"ref36","year":"0","journal-title":"STVC Video Codec"},{"key":"ref35","year":"0","journal-title":"How to join BRATS 2015 Brain Tumor Image Segmentation Challenge"},{"key":"ref34","year":"0","journal-title":"lidc-dataset"},{"key":"ref10","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2015","journal-title":"ICLRE"},{"key":"ref11","first-page":"372","article-title":"The limitations of deep learning in adversarial settings","author":"papernot","year":"2015","journal-title":"1st IEEE European Symposium on Security & Privacy IEEE 2016"},{"key":"ref12","author":"finlayson","year":"2018","journal-title":"Adversarial attacks against medical deep learning systems"},{"key":"ref13","author":"brown","year":"2018","journal-title":"Adversarial Patch"},{"key":"ref14","author":"madry","year":"2017","journal-title":"Towards deep learning models resistant to adversarial attacks"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02628-8_10"},{"key":"ref16","article-title":"Decision-based adversarial attacks: reliable attacks against black-box machine learning models","author":"brendel","year":"2018","journal-title":"2018 ICLR"},{"key":"ref17","author":"paschali","year":"2018","journal-title":"Generalizability vs robustness adversarial examples for medical imaging"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.153"},{"key":"ref19","first-page":"2579","article-title":"Visualizing Data Using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21992"},{"key":"ref4","author":"serj","year":"2018","journal-title":"A deep convolutional neural network for lung cancer diagnostic"},{"key":"ref27","first-page":"682","author":"dance","year":"2014","journal-title":"Diagnostic Radiology Physics A Handbook for Teachers and Students"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1155\/201718314740"},{"key":"ref6","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2014","journal-title":"ICLR abs\/1312 6199"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.11.084"},{"key":"ref5","article-title":"Lung nodule detection using convolutional neural networks","author":"shi","year":"2018","journal-title":"Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No UCBIEECS-2018-27"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140449"},{"key":"ref7","article-title":"Adversarial examples in the physical world","author":"kurakin","year":"2017","journal-title":"ICLR 2017 Workshop track"},{"key":"ref2","author":"kaggle","year":"2017","journal-title":"Data Science Bowl"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10147-017-1131-0"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2018.01.010"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2862403"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1148\/rg.344135128"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.001"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.14132328"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-016-0086-0"},{"key":"ref25","first-page":"779","article-title":"Improving signal to noise ratio of low-dose CT image using wavelet transform","volume":"4","author":"chaudhari","year":"2012","journal-title":"International Journal on Computer Science and Engineering (IJCSE)"}],"event":{"name":"2019 24th Conference of Open Innovations Association (FRUCT)","location":"Moscow, Russia","start":{"date-parts":[[2019,4,8]]},"end":{"date-parts":[[2019,4,12]]}},"container-title":["2019 24th Conference of Open Innovations Association (FRUCT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8700165\/8711873\/08711974.pdf?arnumber=8711974","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T23:45:36Z","timestamp":1559605536000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8711974\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4]]},"references-count":38,"URL":"https:\/\/doi.org\/10.23919\/fruct.2019.8711974","relation":{},"subject":[],"published":{"date-parts":[[2019,4]]}}}