Alexander Binder
Overview
Explore the profile of Alexander Binder including associated specialties, affiliations and a list of published articles.
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Articles
21
Citations
949
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0
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Recent Articles
1.
Srinivasan V, Strodthoff N, Ma J, Binder A, Muller K, Samek W
PLoS One
. 2022 Oct;
17(10):e0274291.
PMID: 36256665
There is an increasing number of medical use cases where classification algorithms based on deep neural networks reach performance levels that are competitive with human medical experts. To alleviate the...
2.
Kumar V, Pouw R, Autio M, Sagmeister M, Phua Z, Borghini L, et al.
Am J Hum Genet
. 2022 Aug;
109(9):1680-1691.
PMID: 36007525
Neisseria meningitidis protects itself from complement-mediated killing by binding complement factor H (FH). Previous studies associated susceptibility to meningococcal disease (MD) with variation in CFH, but the causal variants and...
3.
Chong P, Cheung N, Elovici Y, Binder A
IEEE Trans Image Process
. 2021 Nov;
31:525-540.
PMID: 34793299
When neural networks are employed for high-stakes decision-making, it is desirable that they provide explanations for their prediction in order for us to understand the features that have contributed to...
4.
Hagele M, Seegerer P, Lapuschkin S, Bockmayr M, Samek W, Klauschen F, et al.
Sci Rep
. 2020 Apr;
10(1):6423.
PMID: 32286358
Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative...
5.
Sadek I, Chong P, Rehman S, Elovici Y, Binder A
Data Brief
. 2019 Sep;
26:104437.
PMID: 31528674
This article presents a dataset for studying the detection of obfuscated malware in volatile computer memory. Several obfuscated reverse remote shells were generated using Metasploit-Framework, Hyperion, and PEScrambler tools. After...
6.
Borghini L, Png E, Binder A, Wright V, Pinnock E, de Groot R, et al.
Sci Rep
. 2019 May;
9(1):6966.
PMID: 31061469
Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of...
7.
Lapuschkin S, Waldchen S, Binder A, Montavon G, Samek W, Muller K
Nat Commun
. 2019 Mar;
10(1):1096.
PMID: 30858366
Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly intelligent behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and...
8.
Aung T, Ozaki M, Lee M, Schlotzer-Schrehardt U, Thorleifsson G, Mizoguchi T, et al.
Nat Genet
. 2017 May;
49(7):993-1004.
PMID: 28553957
Exfoliation syndrome (XFS) is the most common known risk factor for secondary glaucoma and a major cause of blindness worldwide. Variants in two genes, LOXL1 and CACNA1A, have previously been...
9.
Samek W, Binder A, Montavon G, Lapuschkin S, Muller K
IEEE Trans Neural Netw Learn Syst
. 2016 Aug;
28(11):2660-2673.
PMID: 27576267
Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not...
10.
Bach S, Binder A, Montavon G, Klauschen F, Muller K, Samek W
PLoS One
. 2015 Jul;
10(7):e0130140.
PMID: 26161953
Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional...