Kayla Robinson
Kayla Robinson
Zweryfikowany adres z uchicago.edu
Tytuł
Cytowane przez
Cytowane przez
Rok
Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography
K Mendel, H Li, D Sheth, M Giger
Academic radiology 26 (6), 735-743, 2019
412019
Variation in algorithm implementation across radiomics software
JJ Foy, KR Robinson, H Li, ML Giger, H Al-Hallaq, SG Armato
Journal of Medical Imaging 5 (4), 044505, 2018
392018
Digital mammography in breast cancer: additive value of radiomics of breast parenchyma
H Li, KR Mendel, L Lan, D Sheth, ML Giger
Radiology 291 (1), 15-20, 2019
352019
Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers’ systems
KR Mendel, H Li, L Lan, CM Cahill, V Rael, H Abe, ML Giger
Journal of Medical Imaging 5 (1), 011002, 2017
142017
Radiomics robustness assessment and classification evaluation: A two‐stage method demonstrated on multivendor FFDM
K Robinson, H Li, L Lan, D Schacht, M Giger
Medical physics 46 (5), 2145-2156, 2019
112019
Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive …
TA Miller, KR Robinson, H Li, TY Seiwert, DJ Haraf, L Lan, ML Giger, ...
Quantitative imaging in medicine and surgery 9 (3), 399, 2019
72019
Transfer learning with convolutional neural networks for lesion classification on clinical breast tomosynthesis
KR Mendel, H Li, D Sheth, ML Giger
Medical Imaging 2018: Computer-Aided Diagnosis 10575, 105750T, 2018
52018
Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations
KR Mendel, H Li, ML Giger
Medical Imaging 2016: Computer-Aided Diagnosis 9785, 97851W, 2016
52016
Deep learning in breast cancer risk assessment: evaluation of fine-tuned convolutional neural networks on a clinical dataset of FFDMs
H Li, KR Mendel, JH Lee, L Lan, ML Giger
Medical Imaging 2018: Computer-Aided Diagnosis 10575, 105750S, 2018
42018
Variations in algorithm implementation among quantitative texture analysis software packages
JJ Foy, P Mitta, LR Nowosatka, KR Mendel, H Li, ML Giger, H Al-Hallaq, ...
Medical Imaging 2018: Computer-Aided Diagnosis 10575, 105751K, 2018
32018
Machine Learning on Medical Imaging for Breast Cancer Risk Assessment
KR Robinson
PQDT-Global, 2019
12019
Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma
H Li, D Sheth, KR Mendel, L Lan, ML Giger
14th International Workshop on Breast Imaging (IWBI 2018) 10718, 1071806, 2018
12018
Impact of Algorithmic Implementation On Quantitative Texture Feature Values: th-ab-201-04
J Foy, K Mendel, H Li, M Giger, H Al-Hallaq, S Armato
Medical Physics 44 (6), 3287, 2017
12017
Temporal mammographic registration for evaluation of architecture changes in cancer risk assessment
K Mendel, H Li, N Tayob, R El-Zein, I Bedrosian, M Giger
Medical Imaging 2019: Computer-Aided Diagnosis 10950, 1095041, 2019
2019
Clustering for Non-Redundant Feature Selection in Radiomics for Breast Cancer Risk Assessment
K Mendel, S Porter, H Li, L Lan, D Schacht, M Giger
MEDICAL PHYSICS 45 (6), E458-E458, 2018
2018
Temporal assessment of radiomic features on clinical mammography in a high-risk population
KR Mendel, H Li, L Lan, CW Chan, LM King, N Tayob, G Whitman, ...
Medical Imaging 2018: Computer-Aided Diagnosis 10575, 105753Q, 2018
2018
Quantitative Radiomics Analysis for Assessment of Breast Cancer Risk
I Bedrosian, N Tayob, L King, R El-Zein, K Mendel, M Giger
ANNALS OF SURGICAL ONCOLOGY 25, S56-S56, 2018
2018
Quantitative Radiomic Texture Analysis: Robustness Across Two Mammography Manufacturers: we-g-601-04
K Mendel, H Li, L Lan, M Giger
Medical Physics 44 (6), 3262, 2017
2017
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