A. Sadeghi-Naini

发表

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence

Muriel Brackstone, Ali Sadeghi-Naini, William T. Tran, 2020, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.

Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics.

S. Tabbarah, F. Lu, W. Tran, 2019, Journal of medical imaging and radiation sciences.

An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI

G. Stanisz, W. Lam, A. Sadeghi-Naini, 2020, Scientific Reports.

Towards clinical prostate ultrasound elastography using full inversion approach.

Abbas Samani, Ali Sadeghi-Naini, Gregory J Czarnota, 2014, Medical physics.

Imaging innovations for cancer therapy response monitoring

Martin J. Yaffe, Gregory J. Czarnota, Michael C. Kolios, 2012 .

A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning

G. Czarnota, A. Sadeghi-Naini, Hadi Moghadas-Dastjerdi, 2020, Scientific Reports.

Early prediction of therapy responses and outcomes in breast cancer patients using textural characteristics of quantitative ultrasound spectral parametric maps

Michael C. Kolios, Maureen, K. Pritchard, 2014 .

Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture

Martin J. Yaffe, Gregory J. Czarnota, Michael C. Kolios, 2014, Oncotarget.

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography

W. Tran, F. Wright, G. Czarnota, 2019, Translational oncology.

Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study

Michael C. Kolios, G. Stanisz, W. Yang, 2020, Cancer medicine.

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies

I. Poon, W. Tran, G. Czarnota, 2020, Future science OA.

Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results

Michael C. Kolios, G. Stanisz, W. Yang, 2020, PloS one.

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer

S. Tabbarah, I. Poon, W. Tran, 2019, Future science OA.

Conventional Frequency Ultrasonic Biomarkers of Cancer Treatment

Michael C. Kolios, W. Tran, O. Falou, 2013 .

Estimation of Lung's Air Volume and Its Variations Throughout Respiratory CT Image Sequences

Rajnikant V. Patel, Abbas Samani, Ting-Yim Lee, 2011, IEEE Transactions on Biomedical Engineering.

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

W. Tran, F. Wright, G. Czarnota, 2018, PloS one.

Predicting Local Failure after Stereotactic Radiation Therapy in Brain Metastasis using Quantitative CT and Machine Learning*

Hany Soliman, Ali Sadeghi-Naini, Majid Jaberipour, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

High-frequency ultrasound detection of cell death: Spectral differentiation of different forms of cell death in vitro

Michael C. Kolios, M. Oelze, G. Czarnota, 2016, Oncoscience.

Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies

F. Lu, W. Tran, A. Sadeghi-Naini, 2022, Scientific Reports.

Quantitative Ultrasound Spectroscopic Imaging for Characterization of Disease Extent in Prostate Cancer Patients1

Michael C. Kolios, Hans T. Chung, M. Yaffe, 2015, Translational oncology.

Quantitative Ultrasound and Cell Death

Michael C. Kolios, O. Falou, G. Czarnota, 2013 .

Ultrasound Elastography of the Prostate Using an Unconstrained Modulus Reconstruction Technique: A Pilot Clinical Study

A. Samani, H. Rivaz, G. Czarnota, 2017, Translational oncology.

Multiparametric monitoring of chemotherapy treatment response in locally advanced breast cancer using quantitative ultrasound and diffuse optical spectroscopy

W. Tran, G. Czarnota, C. Childs, 2016, Oncotarget.

Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models

W. Tran, F. Wright, G. Czarnota, 2019, Translational oncology.

Imaging , Diagnosis , Prognosis Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy

Michael C. Kolios, M. Yaffe, S. Verma, 2013 .

Chemotherapy in Locally Advanced Breast Cancer Patients Receiving Quantitative Ultrasound Evaluation of Tumor Cell Death Response

Michael C. Kolios, M. Yaffe, S. Verma, 2013 .

Quantitative ultrasound visualization of cell death: Emerging clinical applications for detection of cancer treatment response

Omar Falou, Gregory J. Czarnota, Ali Sadeghi-Naini, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity from Breast Radiotherapy Using Supervised Machine Learning.

S. Tabbarah, W. Tran, E. Rakovitch, 2020, International journal of radiation oncology, biology, physics.

Quantitative Ultrasound Characterization of Tumor Cell Death: Ultrasound-Stimulated Microbubbles for Radiation Enhancement

G. Czarnota, A. Sadeghi-Naini, R. Karshafian, 2014, PloS one.

An Enhanced Method for Full-Inversion-Based Ultrasound Elastography of the Liver

A. Samani, A. Sadeghi-Naini, M. Aboutaleb, 2022, 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Data-Efficient Training of Pure Vision Transformers for the Task of Chest X-ray Abnormality Detection Using Knowledge Distillation

A. Sadeghi-Naini, S. Jalalifar, 2022, 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Evaluation of tumor cell death response in locally-advanced breast cancer patients to chemotherapy treatment by scattering property estimates using ultrasound backscatter

M. Oelze, O. Falou, G. Czarnota, 2013 .

A Self-Attention-Guided 3D Deep Residual Network With Big Transfer to Predict Local Failure in Brain Metastasis After Radiotherapy Using Multi-Channel MRI

A. Sahgal, H. Soliman, A. Sadeghi-Naini, 2022, IEEE Journal of Translational Engineering in Health and Medicine.

Toward in vivo lung's tissue incompressibility characterization for tumor motion modeling in radiation therapy.

Abbas Samani, Ali Sadeghi-Naini, Zahra Shirzadi, 2013, Medical physics.

Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis

Gregory J. Czarnota, Elham Karami, Hany Soliman, 2019, Scientific Reports.

Evaluation of neoadjuvant chemotherapy response in women with locally advanced breast cancer using ultrasound elastography.

Ali Sadeghi-Naini, Gregory J Czarnota, Martin J Yaffe, 2013, Translational oncology.

An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI

G. Stanisz, W. Lam, A. Sadeghi-Naini, 2020, Scientific Reports.

Automatic characterization of breast lesions using multi-scale attention-guided deep learning of digital histology images

W. Tran, A. Sadeghi-Naini, Khadijeh Saednia, 2022, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy

G. Czarnota, A. Sadeghi-Naini, S. Jalalifar, 2022, Scientific Reports.

Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment

G. Czarnota, A. Sadeghi-Naini, Hadi Moghadas-Dastjerdi, 2021, Scientific Reports.

Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning

F. Wright, M. Trudeau, G. Czarnota, 2021, Translational oncology.

A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning

G. Czarnota, A. Sadeghi-Naini, Hadi Moghadas-Dastjerdi, 2020, Scientific Reports.

Dielectric properties of the normal and malignant breast tissues in xenograft mice at low frequencies (100 Hz–1 MHz)

A. Samani, G. Czarnota, A. Sadeghi-Naini, 2017 .

Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound

Michael C. Kolios, W. Tran, A. Eisen, 2020, Oncotarget.

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning

A. Sahgal, H. Soliman, A. Sadeghi-Naini, 2021, Scientific Reports.

Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy

Michael C. Kolios, Michael C Kolios, Ali Sadeghi-Naini, 2013, Clinical Cancer Research.

Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features.

A. Sahgal, H. Soliman, A. Sadeghi-Naini, 2022, Medical Physics (Lancaster).

A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy*

Hany Soliman, Ali Sadeghi-Naini, Arjun Sahgal, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

F. Lu, W. Tran, A. Sadeghi-Naini, 2021, Current oncology.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

Gregory J. Czarnota, Ali Sadeghi-Naini, Farnoosh Hadizad, 2017, Scientific Reports.

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

Gregory J. Czarnota, Michael C. Kolios, Ali Sadeghi-Naini, 2017, Scientific Reports.

Early detection of chemotherapy-refractory patients by monitoring textural alterations in diffuse optical spectroscopic images.

Ali Sadeghi-Naini, Gregory J Czarnota, Martin J Yaffe, 2015, Medical physics.

A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks

F. Lu, W. Tran, E. Rakovitch, 2021, Scientific Reports.

An MR Radiomics Framework for Predicting the Outcome of Stereotactic Radiation Therapy in Brain Metastasis*

Elham Karami, Hany Soliman, Mark Ruschin, 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

Mehrdad J. Gangeh, Gregory J. Czarnota, Ali Sadeghi-Naini, 2017, Scientific Reports.

Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach

Gregory J. Czarnota, Ali Sadeghi-Naini, Lakshmanan Sannachi, 2016, Oncotarget.

Non-invasive evaluation of breast cancer response to chemotherapy using quantitative ultrasonic backscatter parameters

Gregory J. Czarnota, Michael Oelze, Ali Sadeghi-Naini, 2015, Medical Image Anal..

Noninvasive Characterization of Locally Advanced Breast Cancer Using Textural Analysis of Quantitative Ultrasound Parametric Images

Gregory J. Czarnota, Ali Sadeghi-Naini, Hadi Tadayyon, 2014, Translational oncology.

Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties.

Ali Sadeghi-Naini, Gregory Czarnota, Hadi Tadayyon, 2014, Medical physics.

Low-frequency quantitative ultrasound imaging of cell death in vivo.

Michael C. Kolios, Michael C Kolios, Ali Sadeghi-Naini, 2013, Medical Physics (Lancaster).

Conventional frequency ultrasonic biomarkers of cancer treatment response in vivo.

Michael C. Kolios, Michael C Kolios, Ali Sadeghi-Naini, 2013, Translational oncology.

Quantitative ultrasound spectral parametric maps: Early surrogates of cancer treatment response

Omar Falou, Gregory J. Czarnota, Ali Sadeghi-Naini, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Quantitative evaluation of cell death response in vitro and in vivo using conventional-frequency ultrasound

Mehrdad J. Gangeh, Gregory J. Czarnota, Ali Sadeghi-Naini, 2015, Oncoscience.

MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

Michael C. Kolios, G. Stanisz, A. Sahgal, 2021, Oncotarget.

Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis

A. Sahgal, H. Soliman, A. Sadeghi-Naini, 2022, Cancers.

Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer

Mehrdad J. Gangeh, Gregory J. Czarnota, Ali Sadeghi-Naini, 2016, IEEE Transactions on Medical Imaging.

Quantification of Ultrasonic Scattering Properties of In Vivo Tumor Cell Death in Mouse Models of Breast Cancer1

Gregory J. Czarnota, Michael C. Kolios, Ali Sadeghi-Naini, 2015, Translational oncology.

Assessment of tumor response to radiation and vascular targeting therapy in mice using quantitative ultrasound spectroscopy.

W. Tran, O. Falou, G. Czarnota, 2015, Medical physics.

Categorizing Extent of Tumor Cell Death Response to Cancer Therapy Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy

Mohamed S. Kamel, Mehrdad J. Gangeh, Gregory J. Czarnota, 2014, IEEE Transactions on Medical Imaging.

Characterizing tumour heterogeneous response to chemotherapy using low-frequency ultrasonic spectroscopy

Gregory J. Czarnota, Ali Sadeghi-Naini, Omar Falou, 2013 .

Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis

Ali Sadeghi-Naini, Mehrdad J Gangeh, Lakshmanan Sannachi, 2017, British Journal of Cancer.

Diffuse optical imaging for monitoring treatment response in breast cancer patients

Omar Falou, Martin J. Yaffe, Gregory J. Czarnota, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Diffuse optical spectroscopy evaluation of treatment response in women with locally advanced breast cancer receiving neoadjuvant chemotherapy.

Hany Soliman, Ali Sadeghi-Naini, Gregory J Czarnota, 2012, Translational oncology.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

W. Tran, G. Czarnota, A. Sadeghi-Naini, 2017, Scientific Reports.

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

Michael C. Kolios, W. Tran, K. Pritchard, 2017, Scientific Reports.

A Brain Tumor Segmentation Framework Based on Outlier Detection Using One-Class Support Vector Machine

Hany Soliman, Ali Sadeghi-Naini, Arjun Sahgal, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

An Automatic Framework for Segmentation of Brain Tumours at Follow-up Scans after Radiation Therapy*

Elham Karami, Hany Soliman, Mark Ruschin, 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Low-frequency ultrasound radiosensitization and therapy response monitoring of tumors: An in vivo study

Gregory J. Czarnota, Ali Sadeghi-Naini, Martin Stanisz, 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients

S. Tabbarah, F. Lu, W. Tran, 2021, Breast Cancer Research and Treatment.

Machine Learning-Based A Priori Chemotherapy Response Prediction in Breast Cancer Patients using Textural CT Biomarkers*

Gregory J. Czarnota, Ali Sadeghi-Naini, Lakshmanan Sannachi, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

A. Samani, A. Kornecki, G. Czarnota, 2023, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

A novel tissue mechanics-based method for improved motion tracking in quasi-static ultrasound elastography.

Sergio C. H. Dempsey, A. Samani, A. Sadeghi-Naini, 2022, Medical physics.

Analytical Estimation of Out-of-plane Strain in Ultrasound Elastography to Improve Axial and Lateral Displacement Fields*

Abbas Samani, Ali Sadeghi-Naini, Niusha Kheirkhah, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Breast Ultrasound Elastography Using Full Inversion-Based Elastic Modulus Reconstruction

Hassan Rivaz, Gregory J. Czarnota, Abbas Samani, 2017, IEEE Transactions on Computational Imaging.

Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer

W. Tran, A. Eisen, F. Wright, 2020, Oncotarget.

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

A. Kiss, S. Tabbarah, F. Lu, 2021, JCO clinical cancer informatics.

Automatic Assessment of Stereotactic Radiation Therapy Outcome in Brain Metastasis Using Longitudinal Segmentation on Serial MRI

A. Sahgal, H. Soliman, A. Sadeghi-Naini, 2023, IEEE Journal of Biomedical and Health Informatics.

Combining First- and Second-Order Continuity Constraints in Ultrasound Elastography

A. Samani, H. Rivaz, A. Sadeghi-Naini, 2021, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

Measurement of Lung Hyperelastic Properties Using Inverse Finite Element Approach

Rajnikant V. Patel, Abbas Samani, Ali Sadeghi-Naini, 2011, IEEE Transactions on Biomedical Engineering.

Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes.

S. Tabbarah, F. Lu, W. Tran, 2023, Breast disease.

In-vivo lung biomechanical modeling for effective tumor motion tracking in external beam radiation therapy

Ting-Yim Lee, Abbas Samani, Elham Karami, 2021, Comput. Biol. Medicine.

A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images

W. Tran, A. Sadeghi-Naini, Khadijeh Saednia, 2022, 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Towards modeling tumor motion in the deflated lung for minimally invasive ablative procedures

Abbas Samani, Ali Sadeghi-Naini, Zahra Shirzadi, 2012, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

An Attention-Guided Deep Neural Network for Annotating Abnormalities in Chest X-ray Images: Visualization of Network Decision Basis*

Ali Sadeghi-Naini, Khadijeh Saednia, Ali Jalalifar, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy

Michael C. Kolios, M. Yaffe, S. Verma, 2013, Clinical Cancer Research.

Quantitative evaluation of cell death response in vitro and in vivo using conventional-frequency ultrasound

O. Falou, G. Czarnota, A. Sadeghi-Naini, 2015, Oncoscience.

Personalization of breast cancer chemotherapy using noninvasive imaging methods to detect tumor cell death responses

Gregory J. Czarnota, Michael C. Kolios, Ali Sadeghi-Naini, 2014 .

Assessment of cancer therapy effects using texton-based characterization of quantitative ultrasound parametric images

Mohamed S. Kamel, Gregory J. Czarnota, Ali Sadeghi-Naini, 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

A real-time trained system for robust speaker verification using relative space of anchor models

Mohammad Mehdi Homayounpour, Abbas Samani, Ali Sadeghi-Naini, 2010, Comput. Speech Lang..