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@article{nokey,

title = {Training Deep Neural Networks with Noisy Clinical Labels: Towards Accurate Detection of Prostate Cancer in US Data},

author = {Golara Javadi and Samareh Samadi and Sharareh Bayat and and Samira Sojoudi and Antonio Hurtado and Walid Eshumani and Silvia Chang and Peter Black and Parvin Mousavi and Purang Abolmaesumi},

year  = {2022},

date = {2022-06-21},

journal = {International Journal of Computer Assisted Radiology and Surgery (IJCARS)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Javadi2021,

title = {Towards Targeted Ultrasound-guided Prostate Biopsy by Incorporating Model and Label Uncertainty in Cancer Detection},

author = {Golara Javadi and Sharareh Bayat and Samareh Samadi and Alireza Sedghi and Samira Sojoudi and Antonio Hurtado and Silvia Chang and Peter Black and Parvin Mousavi and Purang Abolmaesumi},

year  = {2021},

date = {2021-06-22},

journal = {International Journal of Computer Assisted Radiology and Surgery, special issue of International Conference on Information Processing in Computer-Assisted Interventions (IPCAI)},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{javadi2020multiple,

title = {Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study},

author = {Golara Javadi and Samareh Samadi and Sharareh Bayat and Mehran Pesteie and Mohammad H Jafari and Samira Sojoudi and Claudia Kesch and Antonio Hurtado and Silvia Chang and Parvin Mousavi and Peter Black and Purang Abolmaesumi},

year  = {2020},

date = {2020-04-30},

journal = {International Journal of Computer Assisted Radiology and Surgery, special issue of International Conference on Information Processing in Computer-Assisted Interventions (IPCAI)},

volume = {15},

number = {6},

pages = {1023--1031},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{517,

title = {Investigation of Physical Phenomena Underlying Temporal-Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations.},

author = {Sharareh Bayat and Shekoofeh Azizi and Mohammad I Daoud and Guy Nir and Farhad Imani and Carlos D Gerardo and Pingkun Yan and Amir Tahmasebi and Francois Vignon and Samira Sojoudi and Storey Wilson and Kenneth A Iczkowski and Scott M Lucia and Larry Goldenberg and Septimiu E Salcudean and Purang Abolmaesumi and Parvin Mousavi},

doi = {10.1109/TUFFC.2017.2785230},

issn = {1525-8955},

year  = {2017},

date = {2017-12-01},

journal = {IEEE Trans Ultrason Ferroelectr Freq Control},

volume = {65},

pages = {400-410},

abstract = {\<p\>Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro vibrations of tissue due to physiological vibration. In this paper, first, a theoretical formulation for TeUS is presented. Next, a series of simulations are carried out to investigate micro vibration as a source of tissue characterizing information in TeUS. The simulations include finite element modeling of micro vibration in synthetic phantoms, followed by US image generation during TeUS imaging. The simulations are performed on two media, a sparse array of scatterers and a medium with pathology mimicking scatterers that match nuclei distribution extracted from a prostate digital pathology data set. Statistical analysis of the simulated TeUS data shows its ability to accurately classify tissue types. Our experiments suggest that TeUS can capture the microstructural differences, including scatterer density, in tissues as they react to micro vibrations.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{481,

title = {Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations},

author = {Shekoofeh Azizi and Sharareh Bayat and Pingkun Yan and Amir Tahmasebi and Guy Nir and Jin Tae Kwak and Sheng Xu and Storey Wilson and Kenneth A Iczkowski and Scott M Lucia and others},

year  = {2017},

date = {2017-01-01},

journal = {International journal of computer assisted radiology and surgery},

volume = {12},

pages = {1293\textendash1305},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{479,

title = {Investigation of Physical Phenomena Underlying Temporal Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations},

author = {Sharareh Bayat and Shekoofeh Azizi and Mohammad I Daoud and Guy Nir and Farhad Imani and Carlos D Gerardo and Pingkun Yan and Amir Tahmasebi and Francois Vignon and Samira Sojoudi and others},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{524,

title = {Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound.},

author = {Shekoofeh Azizi and Sharareh Bayat and Pingkun Yan and Amir Tahmasebi and Jin Tae Kwak and Sheng Xu and Baris Turkbey and Peter Choyke and Peter Pinto and Bradford Wood and Parvin Mousavi and Purang Abolmaesumi},

doi = {10.1109/TMI.2018.2849959},

issn = {1558-254X},

journal = {IEEE Trans Med Imaging},

volume = {37},

pages = {2695-2703},

abstract = {\<p\>Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we propose to use deep recurrent neural networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that long short-term memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98, and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}