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Cited by in F6Publishing
For: Thoret E, Andrillon T, Léger D, Pressnitzer D. Probing machine-learning classifiers using noise, bubbles, and reverse correlation. J Neurosci Methods 2021;362:109297. [PMID: 34320410 DOI: 10.1016/j.jneumeth.2021.109297] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Sengupta S, Abbey CK, Li K, Anastasio MA. Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment 2022. [DOI: 10.1117/12.2613220] [Reference Citation Analysis]
2 Abbey CK, Sengupta S, Zhou W, Badal A, Zeng R, Samuelson FW, Eckstein MP, Myers KJ, Anastasio MA, Brankov JG. Analyzing neural networks applied to an anatomical simulation of the breast. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment 2022. [DOI: 10.1117/12.2612614] [Reference Citation Analysis]
3 Daube C, Xu T, Zhan J, Webb A, Ince RAA, Garrod OGB, Schyns PG. Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity. Patterns (N Y) 2021;2:100348. [PMID: 34693374 DOI: 10.1016/j.patter.2021.100348] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 8.0] [Reference Citation Analysis]