Conference Proceedings

@inproceedings{Shah_ISMRM2022,

title = {Deep Learning and Fat Fraction Based Analysis of Calf MRI in Diabetic Patients Following Exercise Intervention},

author = {Jill Shah and Tanay Shah and Haresh R. Rajamohan and Katherine Medina and Smita Rao and Cem M. Deniz and Ryan Brown},

url = {https://cds.ismrm.org/protected/22MProceedings/PDFfiles/1136.html},

year  = {2022},

date = {2022-05-11},

booktitle = {Proceedings 30th Scientific Meeting, ISMRM},

pages = {1136},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zhang_ISMRM2022,

title = {Prediction of Total Knee Replacement using Vision Transformers},

author = {Chaojie Zhang and Haresh Rengaraj Rajamohan and Kyunghyun Cho and Gregory Chang and Cem M. Deniz},

url = {https://cds.ismrm.org/protected/22MProceedings/PDFfiles/4431.html},

year  = {2022},

date = {2022-05-11},

booktitle = {Proceedings 30th Scientific Meeting, ISMRM},

pages = {4431},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Dam2022,

title = {Multi-institutional Large-scale Validation Of 8 Methods For Automatic Knee Mri Segmentation For Use In Clinical Trials},

author = {E.B. Dam and A. Desai and C. Deniz and H. Rajamohan and R. Regatte and C. Iriondo and V. Pedoia and S. Majumdar and M. Perslev and C. Igel and A. Pai and S. Gaj and M. Yang and K. Nakamura and X. Li and H. Maqbool and I. Irmakci and S-E. Song and U. Bagci and B. Hargreaves and G. Gold and A. Chaudhari},

doi = {10.1016/j.joca.2022.02.391},

issn = {1063-4584},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

booktitle = {Osteoarthritis and Cartilage},

journal = {Osteoarthritis and Cartilage},

volume = {30},

pages = {S291--S292},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Deniz2022,

title = {Deep Learning Analysis Of Baseline Weightbearing Computed Tomorgraphy For Predicting Radiographic Knee Osteoarthritis Progression},

author = {C. Deniz and N. Segal and V. Sodha and J. Lynch and M. Nevitt and D. Felson and R. Kijowski},

doi = {10.1016/j.joca.2022.02.401},

issn = {1063-4584},

year  = {2022},

date = {2022-04-01},

booktitle = {Osteoarthritis and Cartilage},

journal = {Osteoarthritis and Cartilage},

volume = {30},

pages = {S300},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rajamohan2021,

title = {Deep learning for prediction of incident symptomatic knee OA},

author = {H.R. Rajamohan and Y. Zhou and J. Tan and C.M. Deniz},

url = {https://www.isoai.org/_files/ugd/ac154b_10095b12170a46eea3680c885b9b6d00.pdf},

year  = {2021},

date = {2021-06-30},

urldate = {2021-06-30},

booktitle = {15th International Workshop on Osteoarthritis Imaging},

issuetitle = {15th International Workshop on Osteoarthritis Imaging},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Shah_ISMRM2021,

title = {Deep Learning Based Segmentation and Fat Fraction Assessment of the Calf in Diabetic Subjects and Non-Diabetic Controls},

author = {Jill Shah and Katherine Medina and Haresh R. Rajamohan and Justin ho and Cem M. Deniz and Ryan Brown},

url = {https://cds.ismrm.org/protected/21MProceedings/PDFfiles/0258.html},

year  = {2021},

date = {2021-05-18},

booktitle = {Proceedings 29th Scientific Meeting, ISMRM},

pages = {258},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Desai2020b,

title = {A Report on the International Workshop on Osteoarthritis Imaging Segmentation Challenge: A Multi-Institute Evaluation on a Standard Dataset},

author = {Arjun D. Desai and Francesco Caliva and Claudia Iriondo and Naji Khosravan and Aliasghar Mortazi and Sachin Jambawalikar and Drew Torigian and Jutta Ellerman and Mehmet Akçakaya and Ulas Bagci and Radhika Tibrewala and Io Flament and Matt O'Brien and Sharmila Majumdar and Mathias Perslev and Akshay Pai and Christian Igel and Erik B. Dam and Sibaji Gaj and Mingrui Yang and Kunio Nakamura and Xiaojuan Li and Cem M. Deniz and Vladimir Juras and Ravinder Regatte and Garry E. Gold and Brian A. Hargreaves and Valentina Pedoia and Akshay S. Chaudhari},

url = {https://index.mirasmart.com/ISMRM2020/PDFfiles/1147.html},

year  = {2020},

date = {2020-08-10},

booktitle = {Proceedings 28th Scientific Meeting, ISMRM},

pages = {1147},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kopanoglu2020b,

title = {Simultaneous multi-slice imaging reduces sensitivity of local-SAR to patient motion at 7T},

author = {Emre Kopanoglu and Cem M. Deniz and Richard G. Wise},

url = {https://index.mirasmart.com/ISMRM2020/PDFfiles/3691.html},

year  = {2020},

date = {2020-08-10},

booktitle = {Proceedings 28th Scientific Meeting, ISMRM},

pages = {3691},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zhang2020,

title = {Attention-based CNN for KL Grade Classification: Data from the Osteoarthritis Initiative},

author = {Bofei Zhang and Jimin Tan and Kyunghyun Cho and Gregory Chang and Cem M. Deniz},

doi = {10.1109/ISBI45749.2020.9098456},

issn = {1945-8452},

year  = {2020},

date = {2020-05-22},

booktitle = {2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},

pages = {731-735},

abstract = {Knee osteoarthritis (OA) is a chronic degenerative disorder of joints and it is the most common reason leading to total knee joint replacement. Diagnosis of OA involves subjective judgment on symptoms, medical history, and radiographic readings using Kellgren-Lawrence grade (KL-grade). Deep learning-based methods such as Convolution Neural Networks (CNN) have recently been applied to automatically diagnose radiographic knee OA. In this study, we applied Residual Neural Network (ResNet) to first detect knee joint from radiographs and later combine ResNet with Convolutional Block Attention Module (CBAM) to make a prediction of the KL-grade automatically. The proposed model achieved a multi-class average accuracy of 74.81%, mean squared error of 0.36, and quadratic Kappa score of 0.88, which demonstrates a significant improvement over the published results. The attention maps were analyzed to provide insights on the decision process of the proposed model. Code is available at https://github.com/denizlab/OAI-KL-Grade-Classification.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Desai2020,

title = {A multi-institute automated segmentation evaluation on a standard dataset: Findings from the international workshop on osteoarthritis imaging segmentation challenge},

author = {A. Desai and F. Caliva and C. Iriondo and N. Khosravan and A. Mortazi and S. Jambawalikar and D. Torigian and J. Ellermann and M. Akcakaya and U. Bagci and R. Tibrewala and I. Flament and M. O'Brian and S. Majumdar and M. Perslev and A. Pai and C. Igel and E. Dam and S. Gaj and M. Yang and K. Nakamura and X. Li and Cem M. Deniz and Vladimir Juras and 

R. Regatte and G. Gold and B. Hargreaves and V. Pedoia and A. Chaudhari},

url = {http://www.sciencedirect.com/science/article/pii/S1063458420305446},

doi = {10.1016/j.joca.2020.02.477},

issn = {1063-4584},

year  = {2020},

date = {2020-04-20},

booktitle = {Osteoarthritis and Cartilage},

volume = {28},

pages = {S304 - S305},

note = {Abstracts from the 2020 OARSI World Congress on Osteoarthritis},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}