Dr. Hui Li Profile

Dr. Hui Li

Research Associate Professor at Univ of Chicago

SPIE Involvement:

Editorial Board Member: Journal of Medical Imaging | Author

Publications (49)

Proceedings Article | 3 April 2024 Presentation + Paper

Automatic segmentation of malignant and benign adnexal lesions on ultrasound scans

Heather Whitney, Roni Yoeli-Bik, Jacques Abramowicz, Li Lan, Hui Li, Ryan Longman, Ernst Lengyel, Maryellen Giger

Proceedings Volume 12927, 1292705 (2024) https://doi.org/10.1117/12.3008273

KEYWORDS: Image segmentation, Ultrasonography, Ovarian cancer, Artificial intelligence, Tumors, Cancer, Radiomics

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Proceedings Article | 3 April 2024 Poster + Paper

Automating tumor segmentation and tumor enhancement quantification of I-SPY2 data

Arden Frantzen, Heather Whitney, Hui Li, Karen Drukker, Alexandra Edwards, John Papaioannou, Maryellen Giger

Proceedings Volume 12927, 1292739 (2024) https://doi.org/10.1117/12.3008800

KEYWORDS: Tumors, Feature extraction, Radiomics, Magnetic resonance imaging, Image segmentation, Statistical analysis

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Proceedings Article | 3 April 2024 Presentation + Paper

Using artificial intelligence for chest radiograph interpretation: a retrospective multi-reader-multi-case (MRMC) study of the automatic detection of multiple abnormalities and generation of diagnostic report system

Lin Guo, Guanxun Cheng, Lifei Wang, Bin Zheng, Stefan Jaeger, Maryellen Giger, Jordan Fuhrman, Hui Li, Ajay Divekar, Qian Xiao, Lingjun Qian, Li Xia, Hongjun Li, Fleming Y. Lure

Proceedings Volume 12927, 129270Y (2024) https://doi.org/10.1117/12.3005136

KEYWORDS: Chest imaging, Diagnostics, Artificial intelligence, Pulmonary disorders, Lung, Image fusion, Diseases and disorders, Deep learning, Clinical practice

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SPIE Journal Paper | 21 August 2023 Open Access

Predicting intensive care need for COVID-19 patients using deep learning on chest radiography

Hui Li, Karen Drukker, Qiyuan Hu, Heather Whitney, Jordan Fuhrman, Maryellen Giger

JMI, Vol. 10, Issue 04, 044504, (August 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.044504

KEYWORDS: COVID 19, Chest imaging, Data modeling, Deep learning, Education and training, Performance modeling, Radiography, Medical imaging, Machine learning, Diseases and disorders

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SPIE Journal Paper | 8 July 2023 Open Access

Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect

Natalie Baughan, Hui Li, Li Lan, Matthew Embury, Isaiah Yim, Gary J. Whitman, Randa El-Zein, Isabelle Bedrosian, Maryellen L. Giger

JMI, Vol. 10, Issue 04, 044501, (July 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.044501

KEYWORDS: Mammography, Tumors, Radiography, Radiomics, Deep learning, Breast, Cancer, Breast cancer, Cooccurrence matrices, Histograms

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Proceedings Article | 10 April 2023 Presentation + Paper

User experience evaluation for MIDRC AI interface

Naveena Gorre, Jordan Fuhrman, Eduardo Carranza, Hui Li, Ravi Madduri, Maryellen Giger, Issam El Naqa

Proceedings Volume 12469, 1246904 (2023) https://doi.org/10.1117/12.2651812

KEYWORDS: Artificial intelligence, Data modeling, Machine learning, Visualization, Medical imaging, Visual process modeling, Human-machine interfaces, Decision making, Evolutionary algorithms

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Proceedings Article | 7 April 2023 Presentation + Paper

Novel integration of radiomics and deep transfer learning for diagnosis of indeterminate thyroid nodules on ultrasound

J. Cozzi, H. Li, J. Conn Busch, J. Williams, L. Lan, X. Keutgen, M. Giger

Proceedings Volume 12465, 1246507 (2023) https://doi.org/10.1117/12.2654264

KEYWORDS: Radiomics, Thyroid, Ultrasonography, Deep learning, Pathology, Particle filters, Tumor growth modeling, Education and training, Performance modeling, Feature extraction

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Proceedings Article | 7 April 2023 Presentation + Paper

Assessing an AI-based smart imagery framing and truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest x-ray images for development of deep learning models

Lin Guo, Kunlei Hong, Ziqi Zhang, Bin Zheng, Stefan Jaeger, Jordan Fuhrman, Hui Li, Maryellen Giger, Andrei Gabrielian, Alex Rosenthal, Darrell Hurt, Ziv Yaniv, Y. M. Fleming Lure

Proceedings Volume 12465, 124650R (2023) https://doi.org/10.1117/12.2653826

KEYWORDS: Education and training, Deep learning, Chest imaging, Artificial intelligence, Diseases and disorders, Image segmentation, Data modeling, COVID 19, Machine learning, Lung

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Proceedings Article | 3 April 2023 Presentation + Paper

Longitudinal robustness of a thoracic radiographic AI model for COVID-19 severity prediction

Karen Drukker, Hui Li, Maryellen Giger

Proceedings Volume 12467, 124670C (2023) https://doi.org/10.1117/12.2653636

KEYWORDS: Chest imaging, COVID 19, Performance modeling, Radiography, Machine learning, Diseases and disorders, Artificial intelligence, Data modeling, Image acquisition

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The ‘aging’ of artificial intelligence/machine learning (AI/ML) models after initial development and evaluation is known to frequently occur and can pose substantial problems. When there are changes in population, disease characteristics, imaging equipment, or protocols, model performance may start to deteriorate, and the performance predicted in a research setting may no longer hold after deployment (either in a clinical setting or in further research). This data shift phenomenon is a common problem in AI/ML.
We trained and evaluated a previously in-house developed AI/ML model for COVID severity prediction using two COVID-19-positive consecutive adult patient cohorts from a single institution. The first cohort was from the time that the Delta strain was dominant accounting for <95% of cases (June 24-December 11, 2021, 820 patients, 1331 chest radiographs (CXRs)) and the second cohort was from the time that the Omicron variant was dominant (Jan 1-21, 2022, 656 patients, 970 CXRs). Inclusion criteria were COVID-positivity and the availability of CXR imaging exams, in general for patients not admitted to ICU and prior to ICU admission for those patients admitted to ICU as part of their treatment. Exclusion criteria were image acquisition in ICU or the presence of mechanical ventilation. Our image-based AI/ML model was trained to predict, based on each frontal CXR from a COVID-positive patient, whether this patient would be admitted to ICU within a 24, 48, 72, or 96-hour window. The model was evaluated 1) in a cross-sectional test when trained on a subset/tested on an independent subset of the Delta cohort, 2) similarly for the Omicron cohort, and 3) in a longitudinal test when trained on the Delta cohort/tested on the Omicron cohort.
Cohorts were similar in ICU admission rate and fraction of portable CXRs, while immunization rate was higher for the Omicron cohort. The model did not demonstrate signs of aging with performances in the longitudinal test being very similar to those within the Delta cohort, e.g., an area under the ROC curve in the task of predicting ICU admission within 24 hours of 0.76 [0.68; 0.84] when trained/tested within the Delta cohort and 0.77 [0.73; 0.80] for the longitudinal test (p>0.05). The performance within the Omicron cohort was similar as well, at 0.76 [0.66; 0.84].
Our AI/ML model for COVID-severity prediction did not demonstrate signs of aging in a longitudinal test when trained on the Delta cohort and applied as-is to the Omicron cohort.

Proceedings Article | 3 April 2023 Presentation + Paper

Investigation of demographic implicit discrimination and disparate impact in chest radiography image-based AI for COVID-19 severity prediction

Heather Whitney, Hui Li, Karen Drukker, Michael Reeve, Maryellen Giger

Proceedings Volume 12467, 124670L (2023) https://doi.org/10.1117/12.2654076

KEYWORDS: Artificial intelligence, COVID 19, Chest imaging, Radiography, Deep learning

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SPIE Journal Paper | 6 June 2022

Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset

Hui Li, Heather M. Whitney, Yu Ji, Alexandra Edwards, John Papaioannou, Peifang Liu, Maryellen Giger

JMI, Vol. 9, Issue 03, 034502, (June 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.3.034502

KEYWORDS: Breast, Magnetic resonance imaging, Artificial intelligence, Diagnostics, Drug discovery, Data modeling, Performance modeling, Breast cancer, Tumor growth modeling, Feature selection

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Purpose: We demonstrate continuous learning and assess its impact on the performance of artificial intelligence of breast dynamic contrast-enhanced magnetic resonance imaging in the task of distinguishing malignant from benign lesions on an independent clinical test dataset. Approach: The study included 1979 patients with 1990 lesions who underwent breast MR imaging during 2015, 2016, and 2017, retrospectively collected under an IRB-approved protocol; there were 1494 malignant and 496 benign lesions based on histopathology. AI was conducted in the task of distinguishing malignant and benign lesions, and independent testing was performed to assess the effect of increasing the numbers of training cases. Five training sets mimicking clinical implementation of continuous AI learning included cases from (1) first quarter of 2015, (2) first half of 2015, (3) all 2015, (4) all 2015 and first half of 2016, and (5) all 2015 and 2016. All classifiers were evaluated on the 2017 independent test set. The area under the ROC curve (AUC) served as the performance metric and was calculated over all lesions in the test set, as well as only mass lesions and only non-mass enhancements. The Mann–Kendall test was used to determine if continuous learning resulted in a positive trend in classification performance. P < 0.05 was considered to be statistically significant. Results: Over the continuous training period, the selected feature subsets tended to become more similar and stable. Performance of the five training conditions on the independent test dataset yielded AUCs of 0.86 (95% CI: [0.83,0.90]), 0.87 (95% CI: [0.83,0.90]), 0.88 (95% CI: [0.84,0.91]), 0.89 (95% CI: [0.85,0.92]), and 0.89 (95% CI: [0.86,0.92]). The Mann–Kendall test indicated a statistically significant positive trend (P = 0.0167) in classification performance with continuous learning. Conclusions: Improved diagnostic performance over time was observed when continuous learning of AI was implemented on an independent clinical test dataset.

SPIE Journal Paper | 26 May 2022

A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features

Xavier Keutgen, Hui Li, Kelvin Memeh, Julian Conn Busch, Jelani Williams, Li Lan, David Sarne, Brendan Finnerty, Peter Angelos, Thomas Fahey, Maryellen Giger

JMI, Vol. 9, Issue 03, 034501, (May 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.3.034501

KEYWORDS: Ultrasonography, Fine needle aspiration, Cancer, Surgery, Pathology, Medicine, Cell biology, Diagnostics, Algorithm development, Oncology

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Background: Ultrasound (US)-guided fine needle aspiration (FNA) cytology is the gold standard for the evaluation of thyroid nodules. However, up to 30% of FNA results are indeterminate, requiring further testing. In this study, we present a machine-learning analysis of indeterminate thyroid nodules on ultrasound with the aim to improve cancer diagnosis. Methods: Ultrasound images were collected from two institutions and labeled according to their FNA (F) and surgical pathology (S) diagnoses [malignant (M), benign (B), and indeterminate (I)]. Subgroup breakdown (FS) included: 90 BB, 83 IB, 70 MM, and 59 IM thyroid nodules. Margins of thyroid nodules were manually annotated, and computerized radiomic texture analysis was conducted within tumor contours. Initial investigation was conducted using five-fold cross-validation paradigm with a two-class Bayesian artificial neural networks classifier, including stepwise feature selection. Testing was conducted on an independent set and compared with a commercial molecular testing platform. Performance was evaluated using receiver operating characteristic analysis in the task of distinguishing between malignant and benign nodules. Results: About 1052 ultrasound images from 302 thyroid nodules were used for radiomic feature extraction and analysis. On the training/validation set comprising 263 nodules, five-fold cross-validation yielded area under curves (AUCs) of 0.75 [Standard Error (SE) = 0.04; P < 0.001] and 0.67 (SE = 0.05; P = 0.0012) for the classification tasks of MM versus BB, and IM versus IB, respectively. On an independent test set of 19 IM/IB cases, the algorithm for distinguishing indeterminate nodules yielded an AUC value of 0.88 (SE = 0.09; P < 0.001), which was higher than the AUC of a commercially available molecular testing platform (AUC = 0.81, SE = 0.11; P < 0.005). Conclusion: Machine learning of computer-extracted texture features on gray-scale ultrasound images showed promising results classifying indeterminate thyroid nodules according to their surgical pathology.

Proceedings Article | 4 April 2022 Presentation + Paper

Association between DCE MRI background parenchymal enhancement and mammographic texture features

Natalie Baughan, Lindsay Douglas, Maya Ballard, Esther Seoyeon Lee, Alexandra Edwards, Li Lan, Hui Li, Maryellen Giger

Proceedings Volume 12033, 120330S (2022) https://doi.org/10.1117/12.2613043

KEYWORDS: Mammography, Magnetic resonance imaging, Breast, Breast cancer, Breast imaging, Statistical analysis, Radiology, Cancer, Image analysis

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Proceedings Article | 4 April 2022 Presentation + Paper

Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers

Heather Whitney, Yu Ji, Hui Li, Peifang Liu, Maryellen Giger

Proceedings Volume 12033, 120330T (2022) https://doi.org/10.1117/12.2612205

KEYWORDS: Breast cancer, Artificial intelligence, Cancer, Medical imaging, Magnetic resonance imaging, Computer aided diagnosis and therapy

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A single breast cancer lesion can have different luminal molecular subtyping when using either immunohistochemical (IHC) staining alone or the St. Gallen criteria that includes Ki-67. This may impact artificial intelligence/computer aided diagnosis (AI/CADx) for determining molecular subtype from medical images. We investigated this using 28 radiomic features extracted from DCE-MR images of 877 unique lesions segmented by a fuzzy c-means method, for three groups of lesions: (1) Luminal A lesions by both reference standards (“agreement”), (2) lesions that were Luminal A by IHC and Luminal B by St. Gallen (“disagreement”), and (3) Luminal B lesions by both reference standards (“agreement”). The Kruskal-Wallis (KW) test for statistically significant differences in groups of lesions was sequentially followed by the Mann-Whitney U test to determine pair-wise statistical difference between groups for relevant features from the KW test. Classification of lesions as Luminal A or Luminal B using all available radiomic features was conducted using three sets of lesions: (1) lesions with IHC alone molecular subtyping, (2) lesions with St. Gallen molecular subtyping, and (3) agreement lesions. Five-fold cross-validation using stepwise feature selection/linear discriminant analysis classifier classified lesions in each set, with performance measured by the area under the receiver operating characteristic curve (AUC). Six features (sphericity, irregularity, surface area to volume ratio, variance of radial gradient histogram, sum average, and volume of most enhancing voxels) were significantly different among the three groups of features with mixed difference of the disagreement group of lesions to the two agreement luminal groups. When using agreement lesions, more features were selected for classification and the AUC was significantly higher (P < 0.003) than using lesions subtyped by either reference standard. The results suggest that the disagreement of reference standards may impact the development of medical imaging AI/CADx methods for determining molecular subtype.

SPIE Journal Paper | 1 October 2021 Open Access

Lessons learned in transitioning to AI in the medical imaging of COVID-19

Issam El Naqa, Qiyuan Hu, Weijie Chen, Hui Li, Jordan Fuhrman, Naveena Gorre, Maryellen Giger

JMI, Vol. 8, Issue S1, 010902, (October 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.S1.010902

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SPIE Journal Paper | 24 June 2021 Open Access

Special Section Guest Editorial: Radiogenomics in Prognosis and Treatment

Karen Drukker, Despina Kontos, Hui Li

JMI, Vol. 8, Issue 03, 031901, (June 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.3.031901

KEYWORDS: Genomics, Cancer, Data modeling, Medical imaging, Tumors, Radiology, Genetics, Tumor growth modeling, Image processing, Data archive systems

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Proceedings Article | 15 February 2021 Presentation + Paper

Case-based diagnostic classification repeatability using radiomic features extracted from full-field digital mammography images of breast lesions

Paul Amstutz, Karen Drukker, Hui Li, Hiroyuki Abe, Maryellen Giger, Heather Whitney

Proceedings Volume 11597, 115970X (2021) https://doi.org/10.1117/12.2580743

KEYWORDS: Feature extraction, Image classification, Breast, Digital mammography, Diagnostics, Computer aided diagnosis and therapy, Receivers, Mammography, Image segmentation, Databases

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Radiomic texture analysis for the assessment of osteoporosis on low-dose thoracic CT scans

Jordan Fuhrman, Linnea Kremer, Yeqing Zhu, Rowena Yip, Feng Li, Li Lan, Hui Li, Artit Jirapatnakul, Claudia Henschke, David Yankelevitz, Maryellen Giger

Proceedings Volume 11597, 1159730 (2021) https://doi.org/10.1117/12.2582031

KEYWORDS: Computed tomography, Visualization, Lung cancer, Feature extraction, Visual analytics, Minerals, Databases, Bone

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Proceedings Article | 15 February 2021 Presentation

Radiomic texture analysis of immunofluorescence images of lupus nephritis biopsies to predict patient progression to end-stage renal disease

Bradie Ferguson, Madeleine Durkee, Rebecca Abraham, Junting Ai, Hui Li, Li Lan, Julian Bertini, Marcus Clark, Maryellen Giger

Proceedings Volume 11603, 116030E (2021) https://doi.org/10.1117/12.2582034

KEYWORDS: Biopsy, Image analysis, Biological research, Kidney, Receivers, Luminescence, Inflammation, Image classification, Convolutional neural networks, Confocal microscopy

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Proceedings Article | 15 February 2021 Presentation + Paper

Parenchymal field effect analysis for breast cancer risk assessment: evaluation of FFDM radiomic similarity

Natalie Baughan, Hui Li, Li Lan, Chun-Wai Chan, Matthew Embury, Gary Whitman, Randa El-Zein, Isabelle Bedrosian, Maryellen Giger

Proceedings Volume 11597, 115970Z (2021) https://doi.org/10.1117/12.2581791

KEYWORDS: Tumors, Breast cancer, Breast, Statistical analysis, Quantization, Nipple, Cancer

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Proceedings Article | 16 March 2020 Paper

Preliminary radiomic texture analysis of high-channel fluorescence confocal images of triple-negative breast cancer biopsies

Madeleine Durkee, Bradie Ferguson, Rebecca Abraham, Li Lan, Hui Li, Marcus Clark, Maryellen Giger

Proceedings Volume 11320, 1132013 (2020) https://doi.org/10.1117/12.2549755

KEYWORDS: Tissues, Biopsy, Breast cancer, Inflammation, Confocal microscopy, Statistical analysis, Image classification, Image analysis

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SPIE Journal Paper | 5 March 2020 Open Access

Harmonization of radiomic features of breast lesions across international DCE-MRI datasets

Heather Whitney, Hui Li, Yu Ji, Peifang Liu, Maryellen Giger

JMI, Vol. 7, Issue 01, 012707, (March 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.1.012707

KEYWORDS: Cancer, Breast, Databases, Feature extraction, Computer aided diagnosis and therapy, Medical imaging, Image enhancement, Breast cancer, Solid modeling, Image classification

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Proceedings Article | 13 March 2019 Presentation + Paper

Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer

Rachel Anderson, Hui Li, Yu Ji, Peifang Liu, Maryellen Giger

Proceedings Volume 10950, 1095006 (2019) https://doi.org/10.1117/12.2512667

KEYWORDS: Computer aided diagnosis and therapy, Feature extraction, Magnetic resonance imaging, Breast cancer, Medical imaging, Cancer, Breast, Convolutional neural networks, Databases, Image classification

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Proceedings Article | 13 March 2019 Paper

Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2,397 breast lesions

Heather Whitney, Yu Ji, Hui Li, Alexandra Edwards, John Papaioannou, Peifang Liu, Maryellen Giger

Proceedings Volume 10950, 109501A (2019) https://doi.org/10.1117/12.2512507

KEYWORDS: Breast, Cancer, Feature extraction

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Proceedings Article | 13 March 2019 Paper

Temporal mammographic registration for evaluation of architecture changes in cancer risk assessment

Kayla Mendel, Hui Li, Nabihah Tayob, Randa El-Zein, Isabelle Bedrosian, Maryellen Giger

Proceedings Volume 10950, 1095041 (2019) https://doi.org/10.1117/12.2512792

KEYWORDS: Image registration, Mammography, Breast, Cancer, Breast cancer, Tissues, Error analysis, Image segmentation, Image processing, Image resolution

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SPIE Journal Paper | 4 December 2018 Open Access

Variation in algorithm implementation across radiomics software

Joseph Foy, Kayla Robinson, Hui Li, Maryellen Giger, Hania Al-Hallaq, Samuel Armato

JMI, Vol. 5, Issue 04, 044505, (December 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.4.044505

KEYWORDS: Mammography, Computed tomography, Photovoltaics, Medical imaging, Feature extraction, Image segmentation, Nomenclature, Image processing, Image analysis, Analytical research

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SPIE Journal Paper | 21 August 2018

Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks

Natalia Antropova, Benjamin Huynh, Hui Li, Maryellen L. Giger

JMI, Vol. 6, Issue 01, 011002, (August 2018) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.011002

KEYWORDS: Magnetic resonance imaging, Breast, Image classification, Magnetism, Feature extraction, RGB color model, Tumors, 3D image processing, Tumor growth modeling, Convolutional neural networks

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Proceedings Article | 6 July 2018 Paper

Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma

Hui Li, Deepa Sheth, Kayla Mendel, Li Lan, Maryellen Giger

Proceedings Volume 10718, 1071806 (2018) https://doi.org/10.1117/12.2318282

KEYWORDS: Tumors, Computer aided diagnosis and therapy, Breast, Feature extraction, Breast cancer, Mammography, Statistical analysis, Medical imaging, Image classification

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Proceedings Article | 27 February 2018 Presentation + Paper

Transfer learning with convolutional neural networks for lesion classification on clinical breast tomosynthesis

Kayla Mendel, Hui Li, Deepa Sheth, Maryellen Giger

Proceedings Volume 10575, 105750T (2018) https://doi.org/10.1117/12.2294973

KEYWORDS: Digital breast tomosynthesis, Breast, Convolutional neural networks, Computer aided diagnosis and therapy, Feature extraction, Breast cancer, Digital mammography, Image classification, Databases

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Proceedings Article | 27 February 2018 Presentation + Paper

Deep learning in breast cancer risk assessment: evaluation of fine-tuned convolutional neural networks on a clinical dataset of FFDMs

Hui Li, Kayla Mendel, John Lee, Li Lan, Maryellen Giger

Proceedings Volume 10575, 105750S (2018) https://doi.org/10.1117/12.2294536

KEYWORDS: Breast cancer, Convolutional neural networks, Medical imaging, Feature extraction

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Proceedings Article | 27 February 2018 Paper

Temporal assessment of radiomic features on clinical mammography in a high-risk population

Kayla Mendel, Hui Li, Li Lan, Chun-Wai Chan, Lauren King, Nabihah Tayob, Gary Whitman, Randa El-Zein, Isabelle Bedrosian, Maryellen Giger

Proceedings Volume 10575, 105753Q (2018) https://doi.org/10.1117/12.2293368

KEYWORDS: Cancer, Mammography, Breast, Feature extraction, Breast cancer, Control systems, Medical imaging, Biopsy, Image classification, Tissues

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Proceedings Article | 27 February 2018 Presentation + Paper

Variations in algorithm implementation among quantitative texture analysis software packages

Joseph Foy, Prerana Mitta, Lauren Nowosatka, Kayla Mendel, Hui Li, Maryellen Giger, Hania Al-Hallaq, Samuel Armato

Proceedings Volume 10575, 105751K (2018) https://doi.org/10.1117/12.2292573

KEYWORDS: Image acquisition, Breast, Magnetic resonance imaging, Statistical analysis, Analytical research, Feature extraction, Medical imaging, Computer aided diagnosis and therapy, Quantitative analysis

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SPIE Journal Paper | 19 September 2017

Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers’ systems

Kayla Mendel, Hui Li, Li Lan, Cathleen Cahill, Victoria Rael, Hiroyuki Abe, Maryellen Giger

JMI, Vol. 5, Issue 01, 011002, (September 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011002

KEYWORDS: Manufacturing, Mammography, Digital mammography, Feature extraction, Breast, Image processing, Imaging systems, Fractal analysis, Lithium, Breast cancer

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SPIE Journal Paper | 13 September 2017

Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms

Hui Li, Maryellen Giger, Benjamin Q. Huynh, Natasha Antropova

JMI, Vol. 4, Issue 04, 041304, (September 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.4.041304

KEYWORDS: Breast cancer, Cancer, Feature extraction, Convolutional neural networks, Mammography, Breast, Medical imaging, Lithium, Image classification, Feature selection

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SPIE Journal Paper | 28 December 2016

Breast density estimation from high spectral and spatial resolution MRI

Hui Li, William Weiss, Milica Medved, Hiroyuki Abe, Gillian Newstead, Gregory Karczmar, Maryellen Giger

JMI, Vol. 3, Issue 04, 044507, (December 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.4.044507

KEYWORDS: Breast, Magnetic resonance imaging, Skin, Tissues, Image segmentation, Spatial resolution, Breast cancer, Mammography, Chest, Statistical analysis

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SPIE Journal Paper | 22 August 2016

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

Benjamin Huynh, Hui Li, Maryellen Giger

JMI, Vol. 3, Issue 03, 034501, (August 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.3.034501

KEYWORDS: Convolutional neural networks, Feature extraction, Computer aided diagnosis and therapy, Tumors, Medical imaging, Image segmentation, Lithium, Breast, Digital imaging, Medicine

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Proceedings Article | 24 March 2016 Paper

Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations

Kayla Mendel, Hui Li, Maryellen Giger

Proceedings Volume 9785, 97851W (2016) https://doi.org/10.1117/12.2217775

KEYWORDS: Breast, Tissues, Image segmentation, Magnetic resonance imaging, Breast cancer, Medicine, Mammography, 3D image processing, 3D displays, Cancer, Fuzzy logic

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SPIE Journal Paper | 23 September 2015

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

Wentian Guo, Hui Li, Yitan Zhu, Li Lan, Shengjie Yang, Karen Drukker, Elizabeth Morris, Elizabeth Burnside, Gary Whitman, Maryellen Giger, Yuan Ji, TCGA Breast Phenotype Research Group

JMI, Vol. 2, Issue 04, 041007, (September 2015) https://doi.org/10.1117/12.10.1117/1.JMI.2.4.041007

KEYWORDS: Tumors, Breast, Lymphatic system, Cancer, Receptors, Magnetic resonance imaging, Breast cancer, Statistical analysis, Feature selection, Binary data

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SPIE Journal Paper | 13 November 2014

Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls

Hui Li, Maryellen Giger, Li Lan, Jyothi Janardanan, Charlene Sennett

JMI, Vol. 1, Issue 03, 031009, (November 2014) https://doi.org/10.1117/12.10.1117/1.JMI.1.3.031009

KEYWORDS: Breast, Cancer, Breast cancer, Control systems, Image analysis, Statistical analysis, Mammography, Tissues, Feature selection, Lithium

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Proceedings Article | 9 March 2010 Paper

Effect of variable gain on computerized texture analysis on digitized mammograms

Hui Li, Maryellen Giger, Li Lan, Yading Yuan, Neha Bhooshan, Olufunmilayo Olopade

Proceedings Volume 7624, 76242C (2010) https://doi.org/10.1117/12.845321

KEYWORDS: Breast cancer, Mammography, Breast, Fractal analysis, Feature extraction, Image analysis, Image processing, Scanners, Digital imaging, Receivers

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Proceedings Article | 3 March 2009 Paper

Using three-class BANN classifier in the automated analysis of breast cancer lesions in DCE-MRI

Neha Bhooshan, Maryellen Giger, Darrin Edwards, Karen Drukker, Sanaz Jansen, Hui Li, Li Lan, Gillian Newstead

Proceedings Volume 7260, 72600J (2009) https://doi.org/10.1117/12.813507

KEYWORDS: Breast, Computer aided diagnosis and therapy, Magnetic resonance imaging, Feature selection, Lawrencium, Breast cancer, Databases, Artificial neural networks, Image segmentation, Statistical analysis

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Proceedings Article | 3 March 2009 Paper

Breast cancer classification with mammography and DCE-MRI

Yading Yuan, Maryellen Giger, Hui Li, Charlene Sennett

Proceedings Volume 7260, 72600O (2009) https://doi.org/10.1117/12.813723

KEYWORDS: Mammography, Feature selection, Breast cancer, Image segmentation, Magnetic resonance imaging, Databases, Medical imaging, Computer aided diagnosis and therapy, Current controlled current source, Diagnostics

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Since different imaging modalities provide complementary information regarding the same lesion, combining information from different modalities may increase diagnostic accuracy. In this study, we investigated the use of computerized features of lesions imaged via both full-field digital mammography (FFDM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the classification of breast lesions. Using a manually identified lesion location, i.e. a seed point on FFDM images or a ROI on DCE-MRI images, the computer automatically segmented mass lesions and extracted a set of features for each lesion. Linear stepwise feature selection was firstly performed on single modality, yielding one feature subset for each modality. Then, these selected features served as the input to another feature selection procedure when extracting useful information from both modalities. The selected features were merged by linear discriminant analysis (LDA) into a discriminant score. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the selected feature subset in the task of distinguishing between malignant and benign lesions. From a FFDM database with 321 lesions (167 malignant and 154 benign), and a DCE-MRI database including 181 lesions (97 malignant and 84 benign), we constructed a multi-modality dataset with 51 lesions (29 malignant and 22 benign). With leave-one-out-by-lesion evaluation on the multi-modality dataset, the mammography-only features yielded an area under the ROC curve (AUC) of 0.62 ± 0.08 and the DCE-MRI-only features yielded an AUC of 0.94±0.03. The combination of these two modalities, which included a spiculation feature from mammography and a kinetic feature from DCE-MRI, yielded an AUC of 0.94. The improvement of combining multi-modality information was statistically significant as compared to the use of mammography only (p=0.0001). However, we failed to show the statistically significant improvement as compared to DCE-MRI, with the limited multi-modality dataset (p=0.22).

Proceedings Article | 3 March 2009 Paper

Computerized breast parenchymal analysis on DCE-MRI

Hui Li, Maryellen Giger, Yading Yuan, Sanaz Jansen, Li Lan, Neha Bhooshan, Gillian Newstead

Proceedings Volume 7260, 72600N (2009) https://doi.org/10.1117/12.813567

KEYWORDS: Breast, Breast cancer, Magnetic resonance imaging, Image segmentation, Fuzzy logic, Lithium, Cancer, Medical imaging, 3D image processing, Mammography

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Proceedings Article | 17 March 2008 Paper

Correlative feature analysis of FFDM images

Yading Yuan, Maryellen Giger, Hui Li, Charlene Sennett

Proceedings Volume 6915, 69151L (2008) https://doi.org/10.1117/12.770524

KEYWORDS: Image analysis, Statistical analysis, Image segmentation, Medical imaging, Computer aided diagnosis and therapy, Diagnostics, CAD systems, Breast, Mammography, Tissues

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Proceedings Article | 30 March 2007 Paper

Automatic selection of region of interest for radiographic texture analysis

Li Lan, Maryellen Giger, Joel Wilkie, Tamara Vokes, Weijie Chen, Hui Li, Tracy Lyons, Michael Chinander, Ann Pham

Proceedings Volume 6514, 651436 (2007) https://doi.org/10.1117/12.711531

KEYWORDS: Bone, Image segmentation, Image filtering, Edge detection, Binary data, X-ray imaging, Databases, Computer programming, Minerals, Analytical research

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Proceedings Article | 16 March 2006 Paper

Power spectral analysis of mammographic parenchymal patterns

Hui Li, Maryellen Giger, Olufunmilayo Olopade

Proceedings Volume 6144, 61445J (2006) https://doi.org/10.1117/12.654188

KEYWORDS: Mammography, Breast cancer, Breast, Databases, Spatial frequencies, Receivers, Statistical analysis, Nipple, Cancer, Lithium

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Proceedings Article | 15 March 2006 Paper

A two-stage method for lesion segmentation on digital mammograms

Yading Yuan, Maryellen Giger, Kenji Suzuki, Hui Li, Andrew Jamieson

Proceedings Volume 6144, 61443W (2006) https://doi.org/10.1117/12.652840

KEYWORDS: Image segmentation, Mammography, Algorithm development, Tumors, Gradient-index optics, Computer aided diagnosis and therapy, Fuzzy logic, Lithium, Breast, Medical imaging

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Proceedings Article | 15 May 2003 Paper

Computerized analysis of mammographic parenchymal patterns using fractal analysis

Hui Li, Maryellen Giger, Zhimin Huo, Olufunmilayo Olopade, Michael Chinander, Li Lan, Ioana Bonta

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481359

KEYWORDS: Fractal analysis, Breast cancer, Mammography, Receivers, Image processing, Lithium, Local area networks, Cancer, Breast, Complex systems

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Proceedings Article | 15 May 2003 Paper

Comparison of approaches for risk-modulated CAD

Maryellen Giger, Li Lan, Zhimin Huo, Carl Vyborny, Hui Li

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481395

KEYWORDS: Breast, Feature extraction, Computing systems, Mammography, Computer aided diagnosis and therapy, Databases, Image processing, Breast cancer, Medical imaging, Lithium

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