publications

2021

1. 

   A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos

   Ziming Qiu, Tongda Xu, Jack Langerman, and 8 more authors

   IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2021

   Bib Link

   @article{qiu2021deep,
     title = {A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos},
     author = {Qiu, Ziming and Xu, Tongda and Langerman, Jack and Das, William and Wang, Chuiyu and Nair, Nitin and Aristiz{\'a}bal, Orlando and Mamou, Jonathan and Turnbull, Daniel H and Ketterling, Jeffrey A and others},
     journal = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
     volume = {68},
     number = {7},
     pages = {2460--2471},
     year = {2021},
     publisher = {IEEE},
   }

2. 

   A robust machine learning based framework for the automated detection of ADHD using pupillometric biomarkers and time series analysis

   William Das, and Shubh Khanna

   Scientific reports, 2021

   Bib Link PDF

   @article{das2021robust,
     title = {A robust machine learning based framework for the automated detection of ADHD using pupillometric biomarkers and time series analysis},
     author = {Das, William and Khanna, Shubh},
     journal = {Scientific reports},
     volume = {11},
     number = {1},
     pages = {1--12},
     year = {2021},
     publisher = {Nature Publishing Group},
     link = {https://www.nature.com/articles/s41598-021-95673-5},
   }

3. UN SDSN

   A Machine Learning Approach for the Automated Diagnosis of ADHD: Implications and Significance for Sustainable Youth Development Policies

   William Das, and Shubh Khanna

   In Proceedings of the U.N. SDSN 4th International Conference on Sustainable Development, 2021

   Bib Link

   @inproceedings{das2021machine,
     title = {A Machine Learning Approach for the Automated Diagnosis of ADHD: Implications and Significance for Sustainable Youth Development Policies},
     author = {Das, William and Khanna, Shubh},
     booktitle = {Proceedings of the U.N. SDSN 4th International Conference on Sustainable Development},
     year = {2021},
   }

2020

1. 

   A novel application for the efficient and accessible diagnosis of ADHD using machine learning

   Shubh Khanna, and William Das

   In 2020 IEEE/ITU International Conference on Artificial Intelligence for Good (AI4G), 2020

   Bib Link

   @inproceedings{khanna2020novel,
     title = {A novel application for the efficient and accessible diagnosis of ADHD using machine learning},
     author = {Khanna, Shubh and Das, William},
     booktitle = {2020 IEEE/ITU International Conference on Artificial Intelligence for Good (AI4G)},
     pages = {51--54},
     year = {2020},
     organization = {IEEE},
   }

2. ACM

   A Novel Pupillometric-Based Application for the Automated Detection of ADHD Using Machine Learning

   William Das, and Shubh Khanna

   In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 2020

   Bib Link

   @inproceedings{das2020novel,
     title = {A Novel Pupillometric-Based Application for the Automated Detection of ADHD Using Machine Learning},
     author = {Das, William and Khanna, Shubh},
     booktitle = {Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics},
     pages = {1--6},
     year = {2020},
     link = {https://dl.acm.org/doi/abs/10.1145/3388440.3412427}
   }

3. IEEE ISBI

   Deep mouse: An end-to-end auto-context refinement framework for brain ventricle & body segmentation in embryonic mice ultrasound volumes

   Tongda Xu, Ziming Qiu, William Das, and 8 more authors

   In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020

   Bib Link

   @inproceedings{xu2020deep,
     title = {Deep mouse: An end-to-end auto-context refinement framework for brain ventricle \& body segmentation in embryonic mice ultrasound volumes},
     author = {Xu, Tongda and Qiu, Ziming and Das, William and Wang, Chuiyu and Langerman, Jack and Nair, Nitin and Aristiz{\'a}bal, Orlando and Mamou, Jonathan and Turnbull, Daniel H and Ketterling, Jeffrey A and others},
     booktitle = {2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
     pages = {122--126},
     year = {2020},
     organization = {IEEE},
   }