@article{islam2018comparative,
bibtex_show = {true},
abbr = {IJCA},
title = {Comparative study on machine learning algorithms for sentiment classification},
author = {Islam, Mohammad Mohaiminul and Sultana, Naznin},
journal = {International Journal of Computer Applications},
volume = {182},
number = {21},
pages = {1--7},
html = {https://www.researchgate.net/profile/Mohammad-Islam-86/publication/328357108_Comparative_Study_on_Machine_Learning_Algorithms_for_Sentiment_Classification/links/5bc9a58192851cae21b1fbc0/Comparative-Study-on-Machine-Learning-Algorithms-for-Sentiment-Classification.pdf},
year = {2018}
}
2019
ICASERT
Bangla handwritten character recognition: an overview of the state of the art classification algorithm with new dataset
Hoq, Md Nazmul, Nipa, Nadira Anjum,
Islam, Mohammad Mohaiminul, and Shahriar, Sadat
In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) 2019
@inproceedings{hoq2019bangla,
bibtex_show = {true},
abbr = {ICASERT},
title = {Bangla handwritten character recognition: an overview of the state of the art classification algorithm with new dataset},
author = {Hoq, Md Nazmul and Nipa, Nadira Anjum and Islam, Mohammad Mohaiminul and Shahriar, Sadat},
booktitle = {2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)},
pages = {1--6},
year = {2019},
html = {https://ieeexplore.ieee.org/document/8934641},
organization = {IEEE}
}
2020
IJCCI
Meta classifier-based ensemble learning for sentiment classification
Sultana, Naznin, and Islam, Mohammad Mohaiminul
In Proceedings of International Joint Conference on Computational Intelligence 2020
@inproceedings{hoq2020comparative,
bibtex_show = {true},
abbr = {IJCCI},
title = {A comparative overview of classification algorithm for bangla handwritten digit recognition},
author = {Hoq, Md and Islam, Mohammad Mohaiminul and Nipa, Nadira Anjum and Akbar, Md and others},
booktitle = {Proceedings of international joint conference on computational intelligence},
pages = {265--277},
year = {2020},
html = {https://link.springer.com/chapter/10.1007/978-981-13-7564-4_24},
organization = {Springer}
}
2021
ICEEICT
Interpreting and Comparing Convolutional Neural Networks: A Quantitative Approach
Islam, Mohammad Mohaiminul, and Tushar, Zahid Hassan
In 2021 5th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) 2021
@inproceedings{islam2021interpreting,
bibtex_show = {true},
abbr = {ICEEICT},
html = {https://ieeexplore.ieee.org/document/9667854},
title = {Interpreting and Comparing Convolutional Neural Networks: A Quantitative Approach},
author = {Islam, Mohammad Mohaiminul and Tushar, Zahid Hassan},
booktitle = {2021 5th International Conference on Electrical Engineering and Information \& Communication Technology (ICEEICT)},
pages = {1--6},
year = {2021},
organization = {IEEE}
}
2022
MICCAI
Deep Treatment Response Assessment and Prediction of Colorectal Cancer Liver Metastases
Islam, Mohammad Mohaiminul, Badic, Bogdan, Aparicio, Thomas, Tougeron, David, Tasu, Jean-Pierre, Visvikis, Dimitris, and Conze, Pierre-Henri
In Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
Evaluating treatment response is essential in patients who develop colorectal liver metastases to decide the necessity for second-line treatment or the admissibility for surgery. Currently, RECIST1.1 is the most widely used criteria in this context. However, it involves time-consuming, precise manual delineation and size measurement of main liver metastases from Computed Tomography (CT) images. Moreover, an early prediction of the treatment response given a specific chemotherapy regimen and the initial CT scan would be of tremendous use to clinicians. To overcome these challenges, this paper proposes a deep learning-based treatment response assessment pipeline and its extension for prediction purposes. Based on a newly designed 3D Siamese classification network, our method assigns a response group to patients given CT scans from two consecutive follow-ups during the treatment period. Further, we extended the network to predict the treatment response given only the image acquired at first time point. The pipelines are trained on the PRODIGE20 dataset collected from a phase-II multi-center clinical trial in colorectal cancer with liver metastases and exploit an in-house dataset to integrate metastases delineations derived from a U-Net inspired network as additional information. Our approach achieves overall accuracies of 94.94% and 86.86% for treatment response assessment and early prediction respectively, suggesting that both treatment response assessment and prediction issues can be effectively solved with deep learning.
@inproceedings{10.1007/978-3-031-16437-8_46,
bibtex_show = {true},
html = {https://link.springer.com/chapter/10.1007/978-3-031-16437-8_46#citeas},
abbr = {MICCAI},
author = {Islam, Mohammad Mohaiminul and Badic, Bogdan and Aparicio, Thomas and Tougeron, David and Tasu, Jean-Pierre and Visvikis, Dimitris and Conze, Pierre-Henri},
editor = {Wang, Linwei and Dou, Qi and Fletcher, P. Thomas and Speidel, Stefanie and Li, Shuo},
title = {Deep Treatment Response Assessment and Prediction of Colorectal Cancer Liver Metastases},
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI},
year = {2022},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {482--491},
isbn = {978-3-031-16437-8}
}