UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics
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[1] Michael A Jacobs,et al. Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI , 2017, npj Breast Cancer.
[2] Martin Wattenberg,et al. TCAV: Relative concept importance testing with Linear Concept Activation Vectors , 2018 .
[3] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[4] Dumitru Erhan,et al. The (Un)reliability of saliency methods , 2017, Explainable AI.
[5] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[6] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[7] Henning Müller,et al. Regression Concept Vectors for Bidirectional Explanations in Histopathology , 2018, MLCN/DLF/iMIMIC@MICCAI.
[8] John Kornak,et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis , 2018, npj Breast Cancer.
[9] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[10] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[11] Daniel L Rubin,et al. A curated mammography data set for use in computer-aided detection and diagnosis research , 2017, Scientific Data.
[12] Richard H. Moore,et al. Current Status of the Digital Database for Screening Mammography , 1998, Digital Mammography / IWDM.
[13] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[14] Been Kim,et al. Sanity Checks for Saliency Maps , 2018, NeurIPS.
[15] Richard H. Moore,et al. THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .